├── .gitignore ├── README.md └── books ├── ai-engineering.jpeg ├── ai-engineering.md ├── build-a-large-language-model.jpeg ├── build-a-large-language-model.md ├── build-llm-applications.jpeg ├── build-llm-applications.md ├── building-llm-powered-applications.jpeg ├── building-llm-powered-applications.md ├── building-llms-for-production.jpeg ├── building-llms-for-production.md ├── cover.png ├── creating-production-ready-llms.jpeg ├── creating-production-ready-llms.md ├── developing-apps-with-gpt-4-and-chatgpt.jpeg ├── developing-apps-with-gpt-4-and-chatgpt.md ├── generative-ai-on-aws.md ├── generative-ai-on-aws.png ├── generative-ai-with-langchain.jpeg ├── generative-ai-with-langchain.md ├── hands-on-large-language-models.jpeg ├── hands-on-large-language-models.md ├── langchain-crash-course.jpeg ├── langchain-crash-course.md ├── large-language-models.jpeg ├── large-language-models.md ├── llm-engineer's-handbook.jpeg ├── llm-engineer's-handbook.md ├── llms-in-production.jpeg ├── llms-in-production.md ├── natural-language-processing-with-transformers.jpeg ├── natural-language-processing-with-transformers.md ├── prompt-engineering-for-generative-ai.md ├── prompt-engineering-for-generative-ai.png ├── prompt-engineering-for-llms.jpeg ├── prompt-engineering-for-llms.md ├── quick-start-guide-to-large-language-models.jpeg ├── quick-start-guide-to-large-language-models.md ├── rag-driven-generative-ai.jpeg ├── rag-driven-generative-ai.md ├── super-study-guide.jpeg ├── super-study-guide.md ├── the-developer's-playbook-for-large-language-model-security.jpeg ├── the-developer's-playbook-for-large-language-model-security.md ├── what-is-chatgpt-doing....jpeg └── what-is-chatgpt-doing....md /.gitignore: -------------------------------------------------------------------------------- 1 | .DS_Store -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Awesome LLM Books 2 | 3 | Some of us learn best by reading high quality books on technical topics. 4 | 5 | This is a **curated list** of **books** for **engineers** on **development** with **Large Language Models** (LLMs). 6 | 7 | ## Books: 8 | 9 | Alphabetical list of books on LLMs. Each cover/title links to more information about the book. 10 | 11 | | Cover | Details | 12 | |-------|---------| 13 | | [![AI Engineering](books/ai-engineering.jpeg)](books/ai-engineering.md) | [AI Engineering](books/ai-engineering.md)
**Subtitle**: Building Applications with Foundation Models
**Authors**: Chip Huyen
**Publisher**: O'Reilly, 2025
**Star Rating**: 4.6 on Amazon, 4.56 on Goodreads
**Links**: [Amazon](https://a.co/d/hAI9OXl), [Goodreads](https://www.goodreads.com/book/show/216848047-ai-engineering), [Publisher](https://www.oreilly.com/library/view/ai-engineering/9781098166298/), [GitHub Project](https://github.com/chiphuyen/aie-book) | 14 | | [![Build a Large Language Model](books/build-a-large-language-model.jpeg)](books/build-a-large-language-model.md) | [Build a Large Language Model](books/build-a-large-language-model.md)
**Subtitle**: (From Scratch)
**Authors**: Sebastian Raschka
**Publisher**: Manning, 2024
**Star Rating**: 4.7 on Amazon, 4.64 on Goodreads
**Links**: [Amazon](https://a.co/d/bXGGLyC), [Goodreads](https://www.goodreads.com/book/show/219388329-build-a-large-language-model), [Publisher](https://www.manning.com/books/build-a-large-language-model-from-scratch), [GitHub Project](https://github.com/rasbt/LLMs-from-scratch) | 15 | | [![Build LLM Applications](books/build-llm-applications.jpeg)](books/build-llm-applications.md) | [Build LLM Applications](books/build-llm-applications.md)
**Subtitle**: (from Scratch)
**Authors**: Hamza Farooq
**Publisher**: Manning, 2025
**Links**: [Publisher](https://www.manning.com/books/build-llm-applications-from-scratch) | 16 | | [![Building LLM Powered Applications](books/building-llm-powered-applications.jpeg)](books/building-llm-powered-applications.md) | [Building LLM Powered Applications](books/building-llm-powered-applications.md)
**Subtitle**: Create intelligent apps and agents with large language models
**Authors**: Valentina Alto
**Publisher**: Packt, 2024
**Star Rating**: 4.6 on Amazon, 3.35 on Goodreads
**Links**: [Amazon](https://a.co/d/e6rt1da), [Goodreads](https://www.goodreads.com/book/show/201054993-building-llm-powered-applications), [Publisher](https://www.packtpub.com/en-au/product/building-llm-powered-applications-9781835462317), [GitHub Project](https://github.com/PacktPublishing/Building-LLM-Powered-Applications) | 17 | | [![Building LLMs for Production](books/building-llms-for-production.jpeg)](books/building-llms-for-production.md) | [Building LLMs for Production](books/building-llms-for-production.md)
**Subtitle**: Enhancing LLM Abilities and Reliability with Prompting, Fine-Tuning, and RAG
**Authors**: Louis-François Bouchard and Louie Peters
**Publisher**: Independently published, 2024
**Star Rating**: 4.4 on Amazon, 4.10 on Goodreads
**Links**: [Amazon](https://a.co/d/grz7eTc), [Goodreads](https://www.goodreads.com/book/show/213731760-building-llms-for-production), [Publisher](https://www.oreilly.com/library/view/building-llms-for/9798324731472/) | 18 | | [![Creating Production-Ready LLMs](books/creating-production-ready-llms.jpeg)](books/creating-production-ready-llms.md) | [Creating Production-Ready LLMs](books/creating-production-ready-llms.md)
**Subtitle**: A Comprehensive Guide to Building, Optimizing, and Deploying Large Language Models for Production Use
**Authors**: TransformaTech Institute
**Publisher**: Independently published, 2024
**Star Rating**: 4.5 on Amazon, 0.00 on Goodreads
**Links**: [Amazon](https://a.co/d/7nVhfVT), [Goodreads](https://www.goodreads.com/book/show/219981025-creating-production-ready-llms), [Publisher](https://www.amazon.com.au/stores/author/B0DJRMJX76/about) | 19 | | [![Developing Apps with GPT-4 and ChatGPT](books/developing-apps-with-gpt-4-and-chatgpt.jpeg)](books/developing-apps-with-gpt-4-and-chatgpt.md) | [Developing Apps with GPT-4 and ChatGPT](books/developing-apps-with-gpt-4-and-chatgpt.md)
**Subtitle**: Build Intelligent Chatbots, Content Generators, and More
**Authors**: Olivier Caelen and Marie-Alice Blete
**Publisher**: O'Reilly, 2023
**Star Rating**: 4.2 on Amazon, 3.67 on Goodreads
**Links**: [Amazon](https://a.co/d/8aDJJvi), [Goodreads](https://www.goodreads.com/book/show/181704874-developing-apps-with-gpt-4-and-chatgpt), [Publisher](https://www.oreilly.com/library/view/developing-apps-with/9781098152475/), [GitHub Project](https://github.com/malywut/gpt_examples) | 20 | | [![Generative AI on AWS](books/generative-ai-on-aws.png)](books/generative-ai-on-aws.md) | [Generative AI on AWS](books/generative-ai-on-aws.md)
**Subtitle**: Building Context-Aware Multimodal Reasoning Applications
**Authors**: Chris Fregly, Antje Barth and Shelbee Eigenbrode
**Publisher**: O'Reilly, 2023
**Star Rating**: 4.4 on Amazon, 4.50 on Goodreads
**Links**: [Amazon](https://a.co/d/f6xUdNI), [Goodreads](https://www.goodreads.com/book/show/197525483-generative-ai-on-aws), [Publisher](https://www.oreilly.com/library/view/generative-ai-on/9781098159214/), [GitHub Project](https://github.com/generative-ai-on-aws/generative-ai-on-aws) | 21 | | [![Generative AI with LangChain](books/generative-ai-with-langchain.jpeg)](books/generative-ai-with-langchain.md) | [Generative AI with LangChain](books/generative-ai-with-langchain.md)
**Subtitle**: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs
**Authors**: Ben Auffarth
**Publisher**: Packt, 2023
**Star Rating**: 4.3 on Amazon, 3.50 on Goodreads
**Links**: [Amazon](https://a.co/d/8kVpV3T), [Goodreads](https://www.goodreads.com/book/show/185125672-generative-ai-with-langchain), [Publisher](https://www.packtpub.com/en-us/product/generative-ai-with-langchain-9781835083468), [GitHub Project](https://github.com/benman1/generative_ai_with_langchain) | 22 | | [![Hands-On Large Language Models](books/hands-on-large-language-models.jpeg)](books/hands-on-large-language-models.md) | [Hands-On Large Language Models](books/hands-on-large-language-models.md)
**Subtitle**: Language Understanding and Generation
**Authors**: Jay Alammar and Maarten Grootendorst
**Publisher**: O'Reilly, 2024
**Star Rating**: 4.6 on Amazon, 4.36 on Goodreads
**Links**: [Amazon](https://a.co/d/hXs5jDF), [Goodreads](https://www.goodreads.com/book/show/210408850-hands-on-large-language-models), [Publisher](https://www.oreilly.com/library/view/hands-on-large-language/9781098150952/), [GitHub Project](https://github.com/HandsOnLLM/Hands-On-Large-Language-Models) | 23 | | [![LangChain Crash Course](books/langchain-crash-course.jpeg)](books/langchain-crash-course.md) | [LangChain Crash Course](books/langchain-crash-course.md)
**Subtitle**: Build OpenAI LLM powered Apps: Fast track to building OpenAI LLM powered Apps using Python
**Authors**: Greg Lim
**Publisher**: Independently Published, 2024
**Star Rating**: 4.2 on Amazon, 4.07 on Goodreads
**Links**: [Amazon](https://a.co/d/ibgu6jy), [Goodreads](https://www.goodreads.com/book/show/198671257-langchain-crash-course), [Publisher](https://greglim.gumroad.com/l/langchain) | 24 | | [![Large Language Models](books/large-language-models.jpeg)](books/large-language-models.md) | [Large Language Models](books/large-language-models.md)
**Subtitle**: A Deep Dive: Bridging Theory and Practice
**Authors**: Uday Kamath, Kevin Keenan, Garrett Somers, and Sarah Sorenson
**Publisher**: Springer, 2024
**Star Rating**: 4.1 on Amazon, 4.00 on Goodreads
**Links**: [Amazon](https://a.co/d/6IMNpkX), [Goodreads](https://www.goodreads.com/book/show/214355031-large-language-models), [Publisher](https://link.springer.com/book/10.1007/978-3-031-65647-7), [GitHub Project](https://github.com/springer-llms-deep-dive/llms-deep-dive-tutorials) | 25 | | [![LLM Engineer's Handbook](books/llm-engineer's-handbook.jpeg)](books/llm-engineer's-handbook.md) | [LLM Engineer's Handbook](books/llm-engineer's-handbook.md)
**Subtitle**: Master the art of engineering large language models from concept to production
**Authors**: Paul Iusztin and Maxime Labonne
**Publisher**: Packt, 2024
**Star Rating**: 4.6 on Amazon, 3.54 on Goodreads
**Links**: [Amazon](https://a.co/d/5H3ufht), [Goodreads](https://www.goodreads.com/book/show/216193554-llm-engineer-s-handbook), [Publisher](https://www.packtpub.com/en-au/product/llm-engineers-handbook-9781836200062), [GitHub Project](https://github.com/PacktPublishing/LLM-Engineers-Handbook) | 26 | | [![LLMs in Production](books/llms-in-production.jpeg)](books/llms-in-production.md) | [LLMs in Production](books/llms-in-production.md)
**Subtitle**: From language models to successful products
**Authors**: Christopher Brousseau and Matthew Sharp
**Publisher**: Manning, 2025
**Star Rating**: 4.4 on Amazon, 4.08 on Goodreads
**Links**: [Amazon](https://a.co/d/gF1w56V), [Goodreads](https://www.goodreads.com/book/show/215144443-llms-in-production), [Publisher](https://www.manning.com/books/llms-in-production), [GitHub Project](https://github.com/IMJONEZZ/LLMs-in-Production) | 27 | | [![Natural Language Processing with Transformers](books/natural-language-processing-with-transformers.jpeg)](books/natural-language-processing-with-transformers.md) | [Natural Language Processing with Transformers](books/natural-language-processing-with-transformers.md)
**Subtitle**: Building Language Applications with Hugging Face
**Authors**: Lewis Tunstall, Leandro von Werra and Thomas Wolf
**Publisher**: O'Reilly, 2022
**Star Rating**: 4.6 on Amazon, 4.41 on Goodreads
**Links**: [Amazon](https://a.co/d/5WIiVAC), [Goodreads](https://www.goodreads.com/book/show/60114857-natural-language-processing-with-transformers), [Publisher](https://www.oreilly.com/library/view/natural-language-processing/9781098136789/), [GitHub Project](https://github.com/nlp-with-transformers/notebooks) | 28 | | [![Prompt Engineering for Generative AI](books/prompt-engineering-for-generative-ai.png)](books/prompt-engineering-for-generative-ai.md) | [Prompt Engineering for Generative AI](books/prompt-engineering-for-generative-ai.md)
**Subtitle**: Future-Proof Inputs for Reliable AI Outputs
**Authors**: James Phoenix and Mike Taylor
**Publisher**: O'Reilly, 2024
**Star Rating**: 4.5 on Amazon, 3.56 on Goodreads
**Links**: [Amazon](https://a.co/d/52xLb9K), [Goodreads](https://www.goodreads.com/book/show/204133880-prompt-engineering-for-generative-ai), [Publisher](https://www.oreilly.com/library/view/prompt-engineering-for/9781098153427/), [GitHub Project](https://github.com/BrightPool/prompt-engineering-for-generative-ai-examples) | 29 | | [![Prompt Engineering for LLMs](books/prompt-engineering-for-llms.jpeg)](books/prompt-engineering-for-llms.md) | [Prompt Engineering for LLMs](books/prompt-engineering-for-llms.md)
**Subtitle**: The Art and Science of Building Large Language Model–Based Applications
**Authors**: John Berryman and Albert Ziegler
**Publisher**: O'Reilly, 2024
**Star Rating**: 4.4 on Amazon, 4.00 on Goodreads
**Links**: [Amazon](https://a.co/d/eyWEQ4A), [Goodreads](https://www.goodreads.com/book/show/213739653-prompt-engineering-for-llms), [Publisher](https://www.oreilly.com/library/view/prompt-engineering-for/9781098156145/) | 30 | | [![Quick Start Guide to Large Language Models](books/quick-start-guide-to-large-language-models.jpeg)](books/quick-start-guide-to-large-language-models.md) | [Quick Start Guide to Large Language Models](books/quick-start-guide-to-large-language-models.md)
**Subtitle**: Strategies and Best Practices for ChatGPT, Embeddings, Fine-Tuning, and Multimodal AI
**Authors**: Sinan Ozdemir
**Publisher**: Addison-Wesley, 2024
**Star Rating**: 4.9 on Amazon, 3.64 on Goodreads
**Links**: [Amazon](https://a.co/d/aUsDJ7e), [Goodreads](https://www.goodreads.com/book/show/126850297-quick-start-guide-to-large-language-models), [Publisher](https://www.pearson.com/en-us/subject-catalog/p/quick-start-guide-to-large-language-models-2nd-edition/P200000012793), [GitHub Project](https://github.com/sinanuozdemir/quick-start-guide-to-llms) | 31 | | [![RAG-Driven Generative AI](books/rag-driven-generative-ai.jpeg)](books/rag-driven-generative-ai.md) | [RAG-Driven Generative AI](books/rag-driven-generative-ai.md)
**Subtitle**: Build custom retrieval augmented generation pipelines with LlamaIndex, Deep Lake, and Pinecone
**Authors**: Denis Rothman
**Publisher**: Packt, 2024
**Star Rating**: 4 on Amazon, 3.90 on Goodreads
**Links**: [Amazon](https://a.co/d/2zjaDK4), [Goodreads](https://www.goodreads.com/book/show/214330235-rag-driven-generative-ai), [Publisher](https://www.packtpub.com/en-us/product/rag-driven-generative-ai-9781836200918), [GitHub Project](https://github.com/Denis2054/RAG-Driven-Generative-AI) | 32 | | [![Super Study Guide](books/super-study-guide.jpeg)](books/super-study-guide.md) | [Super Study Guide](books/super-study-guide.md)
**Subtitle**: Transformers & Large Language Models
**Authors**: Afshine Amidi and Shervine Amidi
**Publisher**: Independently published, 2024
**Star Rating**: 4.6 on Amazon, 4.58 on Goodreads
**Links**: [Amazon](https://a.co/d/aE3pz72), [Goodreads](https://www.goodreads.com/book/show/217141763-super-study-guide), [Publisher](https://superstudy.guide/transformers-large-language-models/) | 33 | | [![The Developer's Playbook for Large Language Model Security](books/the-developer's-playbook-for-large-language-model-security.jpeg)](books/the-developer's-playbook-for-large-language-model-security.md) | [The Developer's Playbook for Large Language Model Security](books/the-developer's-playbook-for-large-language-model-security.md)
**Subtitle**: Building Secure AI Applications
**Authors**: Steve Wilson
**Publisher**: O'Reilly, 2024
**Star Rating**: 5 on Amazon, 3.88 on Goodreads
**Links**: [Amazon](https://a.co/d/d3rJVkn), [Goodreads](https://www.goodreads.com/book/show/210408897-the-developer-s-playbook-for-large-language-model-security), [Publisher](https://www.oreilly.com/library/view/the-developers-playbook/9781098162191/) | 34 | | [![What Is ChatGPT Doing...](books/what-is-chatgpt-doing....jpeg)](books/what-is-chatgpt-doing....md) | [What Is ChatGPT Doing...](books/what-is-chatgpt-doing....md)
**Subtitle**: ...and Why Does It Work?
**Authors**: Stephen Wolfram
**Publisher**: Wolfram Media Inc., 2023
**Star Rating**: 4.2 on Amazon, 3.86 on Goodreads
**Links**: [Amazon](https://a.co/d/79xVzR5), [Goodreads](https://www.goodreads.com/book/show/123451665-what-is-chatgpt-doing-and-why-does-it-work), [Publisher](https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-doing-and-why-does-it-work/) | 35 | 36 | ### On Curation 37 | 38 | The above list is not "all books on LLM development", instead it is filtered using the following procedure: 39 | 40 | 1. Create a [master list](https://docs.google.com/spreadsheets/d/1AGExX1aYINy_FsBRmr9z8OyalJcPso2mo28GraHqhSQ/edit?usp=sharing) of all known books on LLM development (amazon, goodreads, google books, etc.) 41 | 2. Read book blurb and table of contents to confirm relevance (for "engineers doing LLM development"). 42 | 3. Read reviews and check star ratings for quality (quality check). 43 | 4. Read comments and discussion about the book on social (twitter/reddit/etc). 44 | 5. Acquire the ebook version of the book, if possible (final read/skim to confirm relevance and quality). 45 | 6. Final judgement call (publisher, gut check). 46 | 47 | Note that I update the list based on newly published books and emails I received about new books. Additionally, listed star ratings are updated periodically. 48 | 49 | ### Make The List Better 50 | 51 | Do you have ideas on how we make this list more awesome? 52 | 53 | Email any time: Jason.Brownlee05@gmail.com -------------------------------------------------------------------------------- /books/ai-engineering.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jason2Brownlee/awesome-llm-books/7aad1f488e993fdf011149a22b2d8a7bae98b007/books/ai-engineering.jpeg -------------------------------------------------------------------------------- /books/ai-engineering.md: -------------------------------------------------------------------------------- 1 | # AI Engineering 2 | 3 | [home](../) 4 | 5 | ![Cover Image](ai-engineering.jpeg) 6 | 7 | ## Details 8 | 9 | * **Title**: AI Engineering 10 | * **Subtitle**: Building Applications with Foundation Models 11 | * **Authors**: Chip Huyen 12 | * **Publication Date**: 2025 13 | * **Publisher**: O'Reilly 14 | * **ISBN-13**: 978-1098166304 15 | * **Pages**: 532 16 | * **Amazon Rating**: 4.6 stars 17 | * **Goodreads Rating**: 4.56 stars 18 | 19 | 20 | **Links**: [Amazon](https://a.co/d/hAI9OXl) | 21 | [Goodreads](https://www.goodreads.com/book/show/216848047-ai-engineering) | 22 | [Publisher](https://www.oreilly.com/library/view/ai-engineering/9781098166298/) | 23 | [GitHub Project](https://github.com/chiphuyen/aie-book) 24 | 25 | ## Blurb 26 | 27 | Recent breakthroughs in AI have not only increased demand for AI products, they've also lowered the barriers to entry for those who want to build AI products. The model-as-a-service approach has transformed AI from an esoteric discipline into a powerful development tool that anyone can use. Everyone, including those with minimal or no prior AI experience, can now leverage AI models to build applications. In this book, author Chip Huyen discusses AI engineering: the process of building applications with readily available foundation models. 28 | 29 | The book starts with an overview of AI engineering, explaining how it differs from traditional ML engineering and discussing the new AI stack. The more AI is used, the more opportunities there are for catastrophic failures, and therefore, the more important evaluation becomes. This book discusses different approaches to evaluating open-ended models, including the rapidly growing AI-as-a-judge approach. 30 | 31 | AI application developers will discover how to navigate the AI landscape, including models, datasets, evaluation benchmarks, and the seemingly infinite number of use cases and application patterns. You'll learn a framework for developing an AI application, starting with simple techniques and progressing toward more sophisticated methods, and discover how to efficiently deploy these applications. 32 | 33 | * Understand what AI engineering is and how it differs from traditional machine learning engineering 34 | * Learn the process for developing an AI application, the challenges at each step, and approaches to address them 35 | * Explore various model adaptation techniques, including prompt engineering, RAG, fine-tuning, agents, and dataset engineering, and understand how and why they work 36 | * Examine the bottlenecks for latency and cost when serving foundation models and learn how to overcome them 37 | * Choose the right model, dataset, evaluation benchmarks, and metrics for your needs 38 | 39 | Chip Huyen works to accelerate data analytics on GPUs at Voltron Data. Previously, she was with Snorkel AI and NVIDIA, founded an AI infrastructure startup, and taught Machine Learning Systems Design at Stanford. She's the author of the book Designing Machine Learning Systems, an Amazon bestseller in AI. 40 | 41 | AI Engineering builds upon and is complementary to Designing Machine Learning Systems (O'Reilly). 42 | 43 | ## Contents 44 | 45 | 1. Introduction to Building AI Applications with Foundation Models 46 | 2. Understanding Foundation Models 47 | 3. Evaluation Methodology 48 | 4. Evaluate AI Systems 49 | 5. Prompt Engineering 50 | 6. RAG and Agents 51 | 7. Finetuning 52 | 8. Dataset Engineering 53 | 9. Inference Optimization 54 | 10. AI Engineering Architecture and User Feedback 55 | -------------------------------------------------------------------------------- /books/build-a-large-language-model.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jason2Brownlee/awesome-llm-books/7aad1f488e993fdf011149a22b2d8a7bae98b007/books/build-a-large-language-model.jpeg -------------------------------------------------------------------------------- /books/build-a-large-language-model.md: -------------------------------------------------------------------------------- 1 | # Build a Large Language Model 2 | 3 | [home](../) 4 | 5 | ![Cover Image](build-a-large-language-model.jpeg) 6 | 7 | ## Details 8 | 9 | * **Title**: Build a Large Language Model 10 | * **Subtitle**: (From Scratch) 11 | * **Authors**: Sebastian Raschka 12 | * **Publication Date**: 2024 13 | * **Publisher**: Manning 14 | * **ISBN-13**: 978-1633437166 15 | * **Pages**: 368 16 | * **Amazon Rating**: 4.7 stars 17 | * **Goodreads Rating**: 4.64 stars 18 | 19 | 20 | **Links**: [Amazon](https://a.co/d/bXGGLyC) | 21 | [Goodreads](https://www.goodreads.com/book/show/219388329-build-a-large-language-model) | 22 | [Publisher](https://www.manning.com/books/build-a-large-language-model-from-scratch) | 23 | [GitHub Project](https://github.com/rasbt/LLMs-from-scratch) 24 | 25 | ## Blurb 26 | 27 | Learn how to create, train, and tweak large language models (LLMs) by building one from the ground up! 28 | 29 | In Build a Large Language Model (from Scratch) bestselling author Sebastian Raschka guides you step by step through creating your own LLM. Each stage is explained with clear text, diagrams, and examples. You’ll go from the initial design and creation, to pretraining on a general corpus, and on to fine-tuning for specific tasks. 30 | 31 | Build a Large Language Model (from Scratch) teaches you how to: 32 | 33 | * Plan and code all the parts of an LLM 34 | * Prepare a dataset suitable for LLM training 35 | * Fine-tune LLMs for text classification and with your own data 36 | * Use human feedback to ensure your LLM follows instructions 37 | * Load pretrained weights into an LLM 38 | 39 | Build a Large Language Model (from Scratch) takes you inside the AI black box to tinker with the internal systems that power generative AI. As you work through each key stage of LLM creation, you’ll develop an in-depth understanding of how LLMs work, their limitations, and their customization methods. Your LLM can be developed on an ordinary laptop, and used as your own personal assistant. 40 | 41 | Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. 42 | 43 | About the technology 44 | 45 | Physicist Richard P. Feynman reportedly said, “I don’t understand anything I can’t build.” Based on this same powerful principle, bestselling author Sebastian Raschka guides you step by step as you build a GPT-style LLM that you can run on your laptop. This is an engaging book that covers each stage of the process, from planning and coding to training and fine-tuning. 46 | 47 | About the book 48 | 49 | Build a Large Language Model (From Scratch) is a practical and eminently-satisfying hands-on journey into the foundations of generative AI. Without relying on any existing LLM libraries, you’ll code a base model, evolve it into a text classifier, and ultimately create a chatbot that can follow your conversational instructions. And you’ll really understand it because you built it yourself! 50 | 51 | What's inside 52 | 53 | * Plan and code an LLM comparable to GPT-2 54 | * Load pretrained weights 55 | * Construct a complete training pipeline 56 | * Fine-tune your LLM for text classification 57 | * Develop LLMs that follow human instructions 58 | 59 | About the reader 60 | 61 | Readers need intermediate Python skills and some knowledge of machine learning. The LLM you create will run on any modern laptop and can optionally utilize GPUs. 62 | 63 | ## Contents 64 | 65 | 1. Understanding large language models 66 | 2. Working with text data 67 | 3. Coding attention mechanisms 68 | 4. Implementing a GPT model from scratch to generate text 69 | 5. Pretraining on unlabeled data 70 | 6. Fine-tuning for classification 71 | 7. Fine-tuning to follow instructions 72 | 8. Introduction to PyTorch 73 | 8. References and further reading 74 | 12. Exercise solutions 75 | 13. Adding bells and whistles to the training loop 76 | 14. Parameter-efficient fine-tuning with LoRA 77 | -------------------------------------------------------------------------------- /books/build-llm-applications.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jason2Brownlee/awesome-llm-books/7aad1f488e993fdf011149a22b2d8a7bae98b007/books/build-llm-applications.jpeg -------------------------------------------------------------------------------- /books/build-llm-applications.md: -------------------------------------------------------------------------------- 1 | # Build LLM Applications 2 | 3 | [home](../) 4 | 5 | ![Cover Image](build-llm-applications.jpeg) 6 | 7 | ## Details 8 | 9 | * **Title**: Build LLM Applications 10 | * **Subtitle**: (from Scratch) 11 | * **Authors**: Hamza Farooq 12 | * **Publication Date**: 2025 13 | * **Publisher**: Manning 14 | * **ISBN-13**: 9781633436527 15 | * **Pages**: 325 16 | 17 | 18 | **Links**: [Publisher](https://www.manning.com/books/build-llm-applications-from-scratch) 19 | 20 | ## Blurb 21 | 22 | Create your own LLM applications without using a framework like LlamaIndex or LangChain. 23 | 24 | In Build LLM Applications (From Scratch), you'll learn to create applications powered by large language models (LLM) from the ground up. In this practical book, you'll build several fully functioning, real-world AI tools—including a search engine, semantic caching for RAG, and autonomous AI agents. 25 | 26 | In Build LLM Applications (From Scratch), you'll learn how to: 27 | 28 | * Design and implement efficient search algorithms for LLM applications 29 | * Develop custom Retrieval Augmented Generation (RAG) systems 30 | * Master deep customization techniques for every aspect of search and RAG components 31 | * Understand and overcome the limitations of popular LLM frameworks 32 | * Create end-to-end LLM solutions by integrating multiple components cohesively 33 | * Apply advanced fine-tuning techniques for task-specific models and domain adaptation 34 | * Deploy quantized versions of open-source LLMs using vLLMs and Ollama 35 | 36 | Build LLM Applications (From Scratch) shows you just how customizable LLM applications can be when you create your own without using opinionated tools like LangChain and LlamaIndex. You'll learn the fundamentals of AI development hands-on, all without any proprietary tools. Soon you'll have the skills you need to build LLM applications, tailor them to your specific needs, and ensure you have control over your entire system. 37 | about the book 38 | 39 | Build LLM Applications (From Scratch) is a practical and comprehensive handbook for creating custom LLM applications without relying on premade frameworks. You'll start by mastering the fundamentals of search systems and RAG. Then you'll apply this knowledge to real-world projects, including building a hotel search engine using TripAdvisor review data, implementing semantic caching in RAG production systems, and deploying a full RAG application using Hugging Face and Gradio. By the end of the book, you'll have the skills to build AI agents from scratch, deploy open source LLMs with advanced quantization techniques, and create innovative, specialized LLM applications designed for your specific needs. 40 | 41 | ## Contents 42 | 43 | PART 1: THE FUNDAMENTALS 44 | * 1. The World of Large Language Models 45 | * 2. An in-depth look into the soul of the Transformer Architecture 46 | * 3. Encoder models in action: Semantic-Based Retrieval Systems 47 | 48 | PART 2: RETRIEVAL SYSTEMS 49 | * 4. Semantic Search from Scratch 50 | * 5. Combining Encoder & Decoder Model to Create RAG Applications 51 | * 6. Advanced RAG techniques with knowledge graphs and Semantic Cache 52 | 53 | PART 3: BUILDING ENTERPRISE LLM APPLICATION 54 | * 7. Introducing Agents, the next Generation of AI 55 | * 8. Fine-Tuning and Domain Adapation 56 | * 9. Deploying Language Models as APIs 57 | * 10. OpenSource Large Language Models 58 | -------------------------------------------------------------------------------- /books/building-llm-powered-applications.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jason2Brownlee/awesome-llm-books/7aad1f488e993fdf011149a22b2d8a7bae98b007/books/building-llm-powered-applications.jpeg -------------------------------------------------------------------------------- /books/building-llm-powered-applications.md: -------------------------------------------------------------------------------- 1 | # Building LLM Powered Applications 2 | 3 | [home](../) 4 | 5 | ![Cover Image](building-llm-powered-applications.jpeg) 6 | 7 | ## Details 8 | 9 | * **Title**: Building LLM Powered Applications 10 | * **Subtitle**: Create intelligent apps and agents with large language models 11 | * **Authors**: Valentina Alto 12 | * **Publication Date**: 2024 13 | * **Publisher**: Packt 14 | * **ISBN-13**: 978-1835462317 15 | * **Pages**: 342 16 | * **Amazon Rating**: 4.6 stars 17 | * **Goodreads Rating**: 3.35 stars 18 | 19 | 20 | **Links**: [Amazon](https://a.co/d/e6rt1da) | 21 | [Goodreads](https://www.goodreads.com/book/show/201054993-building-llm-powered-applications) | 22 | [Publisher](https://www.packtpub.com/en-au/product/building-llm-powered-applications-9781835462317) | 23 | [GitHub Project](https://github.com/PacktPublishing/Building-LLM-Powered-Applications) 24 | 25 | ## Blurb 26 | 27 | Get hands-on with GPT 3.5, GPT 4, LangChain, Llama 2, Falcon LLM and more, to build LLM-powered sophisticated AI applications 28 | 29 | Key Features 30 | * Embed LLMs into real-world applications 31 | * Use LangChain to orchestrate LLMs and their components within applications 32 | * Grasp basic and advanced techniques of prompt engineering 33 | 34 | Book Description 35 | Building LLM Powered Applications delves into the fundamental concepts, cutting-edge technologies, and practical applications that LLMs offer, ultimately paving the way for the emergence of large foundation models (LFMs) that extend the boundaries of AI capabilities. 36 | 37 | The book begins with an in-depth introduction to LLMs. We then explore various mainstream architectural frameworks, including both proprietary models (GPT 3.5/4) and open-source models (Falcon LLM), and analyze their unique strengths and differences. Moving ahead, with a focus on the Python-based, lightweight framework called LangChain, we guide you through the process of creating intelligent agents capable of retrieving information from unstructured data and engaging with structured data using LLMs and powerful toolkits. Furthermore, the book ventures into the realm of LFMs, which transcend language modeling to encompass various AI tasks and modalities, such as vision and audio. 38 | 39 | Whether you are a seasoned AI expert or a newcomer to the field, this book is your roadmap to unlock the full potential of LLMs and forge a new era of intelligent machines. 40 | 41 | What you will learn 42 | * Explore the core components of LLM architecture, including encoder-decoder blocks and embeddings 43 | * Understand the unique features of LLMs like GPT-3.5/4, Llama 2, and Falcon LLM 44 | * Use AI orchestrators like LangChain, with Streamlit for the frontend 45 | * Get familiar with LLM components such as memory, prompts, and tools 46 | * Learn how to use non-parametric knowledge and vector databases 47 | * Understand the implications of LFMs for AI research and industry applications 48 | * Customize your LLMs with fine tuning 49 | * Learn about the ethical implications of LLM-powered applications 50 | 51 | Who this book is for 52 | Software engineers and data scientists who want hands-on guidance for applying LLMs to build applications. The book will also appeal to technical leaders, students, and researchers interested in applied LLM topics. 53 | 54 | We don’t assume previous experience with LLM specifically. But readers should have core ML/software engineering fundamentals to understand and apply the content. 55 | 56 | ## Contents 57 | 58 | 1. Introduction to Large Language Models 59 | 2. LLMs for AI-Powered Applications 60 | 3. Choosing an LLM for Your Application 61 | 4. Prompt Engineering 62 | 5. Embedding LLMs within Your Applications 63 | 6. Building Conversational Applications 64 | 7. Search and Recommendation Engines with LLMs 65 | 8. Using LLMs with Structured Data 66 | 9. Working with Code 67 | 10. Building Multimodal Applications with LLMs 68 | 11. Fine-Tuning Large Language Models 69 | 12. Responsible AI 70 | 13. Emerging Trends and Innovations 71 | -------------------------------------------------------------------------------- /books/building-llms-for-production.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jason2Brownlee/awesome-llm-books/7aad1f488e993fdf011149a22b2d8a7bae98b007/books/building-llms-for-production.jpeg -------------------------------------------------------------------------------- /books/building-llms-for-production.md: -------------------------------------------------------------------------------- 1 | # Building LLMs for Production 2 | 3 | [home](../) 4 | 5 | ![Cover Image](building-llms-for-production.jpeg) 6 | 7 | ## Details 8 | 9 | * **Title**: Building LLMs for Production 10 | * **Subtitle**: Enhancing LLM Abilities and Reliability with Prompting, Fine-Tuning, and RAG 11 | * **Authors**: Louis-François Bouchard and Louie Peters 12 | * **Publication Date**: 2024 13 | * **Publisher**: Independently published 14 | * **ISBN-13**: 979-8324731472 15 | * **Pages**: 463 16 | * **Amazon Rating**: 4.4 stars 17 | * **Goodreads Rating**: 4.10 stars 18 | 19 | 20 | **Links**: [Amazon](https://a.co/d/grz7eTc) | 21 | [Goodreads](https://www.goodreads.com/book/show/213731760-building-llms-for-production) | 22 | [Publisher](https://www.oreilly.com/library/view/building-llms-for/9798324731472/) 23 | 24 | ## Blurb 25 | 26 | With amazing feedback from industry leaders, this book is an end-to-end resource for anyone looking to enhance their skills or dive into the world of AI and develop their understanding of Generative AI and Large Language Models (LLMs). It explores various methods to adapt "foundational" LLMs to specific use cases with enhanced accuracy, reliability, and scalability. Written by over 10 people on our Team at Towards AI and curated by experts from Activeloop, LlamaIndex, Mila, and more, it is a roadmap to the tech stack of the future. 27 | 28 | The book aims to guide developers through creating LLM products ready for production, leveraging the potential of AI across various industries. It is tailored for readers with an intermediate knowledge of Python. 29 | 30 | 31 | What's Inside this 470-page Book (Updated October 2024)? 32 | 33 | * Hands-on Guide on LLMs, Prompting, Retrieval Augmented Generation (RAG) & Fine-tuning 34 | * Roadmap for Building Production-Ready Applications using LLMs 35 | * Fundamentals of LLM Theory 36 | * Simple-to-Advanced LLM Techniques & Frameworks 37 | * Code Projects with Real-World Applications 38 | * Colab Notebooks that you can run right away 39 | * Community access and our own AI Tutor 40 | 41 | Whether you're looking to enhance your skills or dive into the world of AI for the first time as a programmer or software student, our book is for you. From the basics of LLMs to mastering fine-tuning and RAG for scalable, reliable AI applications, we guide you every step of the way. 42 | 43 | ## Contents 44 | 45 | 1. Introduction to Large Language Models 46 | 2. LLM Architectures & Landscape 47 | 3. LLMs in Practice 48 | 4. Introduction to Prompting 49 | 5. Retrieval-Augmented Generation 50 | 6. Introduction to LangChain & LlamaIndex 51 | 7. Prompting with LangChain 52 | 8. Indexes, Retrievers, and Data Preparation 53 | 9. Advanced RAG 54 | 10. Agents 55 | 11. Fine-Tuning 56 | 12. Deployment and Optimization 57 | -------------------------------------------------------------------------------- /books/cover.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jason2Brownlee/awesome-llm-books/7aad1f488e993fdf011149a22b2d8a7bae98b007/books/cover.png -------------------------------------------------------------------------------- /books/creating-production-ready-llms.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jason2Brownlee/awesome-llm-books/7aad1f488e993fdf011149a22b2d8a7bae98b007/books/creating-production-ready-llms.jpeg -------------------------------------------------------------------------------- /books/creating-production-ready-llms.md: -------------------------------------------------------------------------------- 1 | # Creating Production-Ready LLMs 2 | 3 | [home](../) 4 | 5 | ![Cover Image](creating-production-ready-llms.jpeg) 6 | 7 | ## Details 8 | 9 | * **Title**: Creating Production-Ready LLMs 10 | * **Subtitle**: A Comprehensive Guide to Building, Optimizing, and Deploying Large Language Models for Production Use 11 | * **Authors**: TransformaTech Institute 12 | * **Publication Date**: 2024 13 | * **Publisher**: Independently published 14 | * **ISBN-13**: 979-8341060043 15 | * **Pages**: 546 16 | * **Amazon Rating**: 4.5 stars 17 | * **Goodreads Rating**: 0.00 stars 18 | 19 | 20 | **Links**: [Amazon](https://a.co/d/7nVhfVT) | 21 | [Goodreads](https://www.goodreads.com/book/show/219981025-creating-production-ready-llms) | 22 | [Publisher](https://www.amazon.com.au/stores/author/B0DJRMJX76/about) 23 | 24 | ## Blurb 25 | 26 | Master the Art of Building, Optimizing, and Deploying Large Language Models for Production 27 | 28 | The ONLY resource you will need to develop LLMs that can thrive in real-world applications. 29 | 30 | This 500+ page guide covers everything from foundational concepts to advanced techniques, offering a clear roadmap for: 31 | * Understanding the core architectures behind LLMs, including transformers, GPT, BERT, and T5. 32 | * Training models from scratch, optimizing performance, and implementing distributed training with multi-GPU and TPU setups. 33 | * Fine-tuning pre-trained models for specific tasks and ensuring they are reliable, scalable, and efficient. 34 | * Practical strategies for integrating LLMs into business workflows, including case studies from industries like healthcare, finance, and education. 35 | * Addressing key challenges such as debugging, handling edge cases, and ensuring robust security and ethical compliance. 36 | * Mastering prompt engineering to enhance model performance, generate precise outputs, and unlock the full potential of LLMs in real-world applications. 37 | 38 | By the end of this book, you will have the expertise to: 39 | * Take LLMs from concept to production use with confidence. 40 | * Deploy LLMs in high-demand, real-world environments. 41 | * Solve challenges in scaling, optimizing, and maintaining LLMs in production. 42 | * Understand key ethical considerations and how to mitigate bias in LLM deployments. 43 | 44 | This book goes beyond theory, providing hands-on examples, case studies, and real-world insights that will help you apply LLMs effectively in your projects. Whether you're an AI engineer, data scientist, researcher, or business leader, Production-Ready LLMs equips you with the tools to stay ahead in the fast-paced world of AI. 45 | 46 | If you’re ready to move beyond experimentation and develop LLMs that deliver results in real-world scenarios, Production-Ready LLMs is your essential companion. 47 | 48 | ## Contents 49 | 50 | 1. Understanding Language Models 51 | 2. Architectures and Frameworks 52 | 3. The Mathematics behind LLMs 53 | 4. Data Collection and Preprocessing 54 | 5. Traingn LLMs from Scratch 55 | 6. Fine Tuning Pre-trained Models 56 | 7. Prompt Engineering 57 | 8. Retrieval-Augmented Generation (RAG) 58 | 9. Model Optimization for Production 59 | 10. Debugging and Trouble Shooing LLMs 60 | 11. Production-Ready LLMs 61 | 12. Security and Ethical Considerations 62 | 13. Integrating LLMs w ith Business Applications 63 | 14. LLMs in Healthcare 64 | 15. LLMs in Finance 65 | 16. LLMs in Education 66 | 17. Emerging Trends and Technologies 67 | 18. Conclusion and Final Thoughts 68 | -------------------------------------------------------------------------------- /books/developing-apps-with-gpt-4-and-chatgpt.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jason2Brownlee/awesome-llm-books/7aad1f488e993fdf011149a22b2d8a7bae98b007/books/developing-apps-with-gpt-4-and-chatgpt.jpeg -------------------------------------------------------------------------------- /books/developing-apps-with-gpt-4-and-chatgpt.md: -------------------------------------------------------------------------------- 1 | # Developing Apps with GPT-4 and ChatGPT 2 | 3 | [home](../) 4 | 5 | ![Cover Image](developing-apps-with-gpt-4-and-chatgpt.jpeg) 6 | 7 | ## Details 8 | 9 | * **Title**: Developing Apps with GPT-4 and ChatGPT 10 | * **Subtitle**: Build Intelligent Chatbots, Content Generators, and More 11 | * **Authors**: Olivier Caelen and Marie-Alice Blete 12 | * **Publication Date**: 2023 13 | * **Publisher**: O'Reilly 14 | * **ISBN-13**: 978-1098152482 15 | * **Pages**: 155 16 | * **Amazon Rating**: 4.2 stars 17 | * **Goodreads Rating**: 3.67 stars 18 | 19 | 20 | **Links**: [Amazon](https://a.co/d/8aDJJvi) | 21 | [Goodreads](https://www.goodreads.com/book/show/181704874-developing-apps-with-gpt-4-and-chatgpt) | 22 | [Publisher](https://www.oreilly.com/library/view/developing-apps-with/9781098152475/) | 23 | [GitHub Project](https://github.com/malywut/gpt_examples) 24 | 25 | ## Blurb 26 | 27 | This minibook is a comprehensive guide for Python developers who want to learn how to build applications with large language models. Authors Olivier Caelen and Marie-Alice Blete cover the main features and benefits of GPT-4 and ChatGPT and explain how they work. You'll also get a step-by-step guide for developing applications using the GPT-4 and ChatGPT Python library, including text generation, Q&A, and content summarization tools. 28 | 29 | Written in clear and concise language, Developing Apps with GPT-4 and ChatGPT includes easy-to-follow examples to help you understand and apply the concepts to your projects. Python code examples are available in a GitHub repository, and the book includes a glossary of key terms. Ready to harness the power of large language models in your applications? This book is a must. 30 | 31 | You'll learn: 32 | 33 | * The fundamentals and benefits of ChatGPT and GPT-4 and how they work 34 | * How to integrate these models into Python-based applications for NLP tasks 35 | * How to develop applications using GPT-4 or ChatGPT APIs in Python for text generation, question answering, and content summarization, among other tasks 36 | * Advanced GPT topics including prompt engineering, fine-tuning models for specific tasks, plug-ins, LangChain, and more 37 | 38 | ## Contents 39 | 40 | 1. GPT-4 and ChatGPT Essentials 41 | 2. A Deep Dive into the GPT-4 and ChatGPT APIs 42 | 3. Building Apps with GPT-4 and ChatGPT 43 | 4. Advanced GPT-4 and ChatGPT Techniques 44 | 5. Advancing LLM Capabilities with the LangChain Framework and Plug-ins 45 | -------------------------------------------------------------------------------- /books/generative-ai-on-aws.md: -------------------------------------------------------------------------------- 1 | # Generative AI on AWS 2 | 3 | [home](../) 4 | 5 | ![Cover Image](generative-ai-on-aws.png) 6 | 7 | ## Details 8 | 9 | * **Title**: Generative AI on AWS 10 | * **Subtitle**: Building Context-Aware Multimodal Reasoning Applications 11 | * **Authors**: Chris Fregly, Antje Barth and Shelbee Eigenbrode 12 | * **Publication Date**: 2023 13 | * **Publisher**: O'Reilly 14 | * **ISBN-13**: 978-1098159221 15 | * **Pages**: 309 16 | * **Amazon Rating**: 4.4 stars 17 | * **Goodreads Rating**: 4.50 stars 18 | 19 | 20 | **Links**: [Amazon](https://a.co/d/f6xUdNI) | 21 | [Goodreads](https://www.goodreads.com/book/show/197525483-generative-ai-on-aws) | 22 | [Publisher](https://www.oreilly.com/library/view/generative-ai-on/9781098159214/) | 23 | [GitHub Project](https://github.com/generative-ai-on-aws/generative-ai-on-aws) 24 | 25 | ## Blurb 26 | 27 | Companies today are moving rapidly to integrate generative AI into their products and services. But there's a great deal of hype (and misunderstanding) about the impact and promise of this technology. With this book, Chris Fregly, Antje Barth, and Shelbee Eigenbrode from AWS help CTOs, ML practitioners, application developers, business analysts, data engineers, and data scientists find practical ways to use this exciting new technology. 28 | 29 | You'll learn the generative AI project life cycle including use case definition, model selection, model fine-tuning, retrieval-augmented generation, reinforcement learning from human feedback, and model quantization, optimization, and deployment. And you'll explore different types of models including large language models (LLMs) and multimodal models such as Stable Diffusion for generating images and Flamingo/IDEFICS for answering questions about images. 30 | 31 | * Apply generative AI to your business use cases 32 | * Determine which generative AI models are best suited to your task 33 | * Perform prompt engineering and in-context learning 34 | * Fine-tune generative AI models on your datasets with low-rank adaptation (LoRA) 35 | * Align generative AI models to human values with reinforcement learning from human feedback (RLHF) 36 | * Augment your model with retrieval-augmented generation (RAG) 37 | * Explore libraries such as LangChain and ReAct to develop agents and actions 38 | * Build generative AI applications with Amazon Bedrock 39 | 40 | ## Contents 41 | 42 | 1. Generative AI Use Cases, Fundamentals, Project Lifecycle 43 | 2. Prompt Engineering and In-Context Learning 44 | 3. Large-Language Foundation Models 45 | 4. Quantization and Distributed Computing 46 | 5. Fine-Tuning and Evaluation 47 | 6. Parameter-efficient Fine Tuning (PEFT) 48 | 7. Fine-tuning using Reinforcement Learning with RLHF 49 | 8. Optimize and Deploy Generative AI Applications 50 | 9. Retrieval Augmented Generation (RAG) and Agents 51 | 10. Multimodal Foundation Models 52 | 11. Controlled Generation and Fine-Tuning with Stable Diffusion 53 | 12. Amazon Bedrock Managed Service for Generative AI 54 | -------------------------------------------------------------------------------- /books/generative-ai-on-aws.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jason2Brownlee/awesome-llm-books/7aad1f488e993fdf011149a22b2d8a7bae98b007/books/generative-ai-on-aws.png -------------------------------------------------------------------------------- /books/generative-ai-with-langchain.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jason2Brownlee/awesome-llm-books/7aad1f488e993fdf011149a22b2d8a7bae98b007/books/generative-ai-with-langchain.jpeg -------------------------------------------------------------------------------- /books/generative-ai-with-langchain.md: -------------------------------------------------------------------------------- 1 | # Generative AI with LangChain 2 | 3 | [home](../) 4 | 5 | ![Cover Image](generative-ai-with-langchain.jpeg) 6 | 7 | ## Details 8 | 9 | * **Title**: Generative AI with LangChain 10 | * **Subtitle**: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs 11 | * **Authors**: Ben Auffarth 12 | * **Publication Date**: 2023 13 | * **Publisher**: Packt 14 | * **ISBN-13**: 978-1835083468 15 | * **Pages**: 360 16 | * **Amazon Rating**: 4.3 stars 17 | * **Goodreads Rating**: 3.50 stars 18 | 19 | 20 | **Links**: [Amazon](https://a.co/d/8kVpV3T) | 21 | [Goodreads](https://www.goodreads.com/book/show/185125672-generative-ai-with-langchain) | 22 | [Publisher](https://www.packtpub.com/en-us/product/generative-ai-with-langchain-9781835083468) | 23 | [GitHub Project](https://github.com/benman1/generative_ai_with_langchain) 24 | 25 | ## Blurb 26 | 27 | Key Features 28 | * Learn how to leverage LangChain to work around LLMs’ inherent weaknesses 29 | * Delve into LLMs with LangChain and explore their fundamentals, ethical dimensions, and application challenges 30 | * Get better at using ChatGPT and GPT models, from heuristics and training to scalable deployment, empowering you to transform ideas into reality 31 | 32 | Book Description 33 | ChatGPT and the GPT models by OpenAI have brought about a revolution not only in how we write and research but also in how we can process information. This book discusses the functioning, capabilities, and limitations of LLMs underlying chat systems, including ChatGPT and Gemini. It demonstrates, in a series of practical examples, how to use the LangChain framework to build production-ready and responsive LLM applications for tasks ranging from customer support to software development assistance and data analysis – illustrating the expansive utility of LLMs in real-world applications. 34 | 35 | Unlock the full potential of LLMs within your projects as you navigate through guidance on fine-tuning, prompt engineering, and best practices for deployment and monitoring in production environments. Whether you're building creative writing tools, developing sophisticated chatbots, or crafting cutting-edge software development aids, this book will be your roadmap to mastering the transformative power of generative AI with confidence and creativity. 36 | 37 | What you will learn 38 | * Create LLM apps with LangChain, like question-answering systems and chatbots 39 | * Understand transformer models and attention mechanisms 40 | * Automate data analysis and visualization using pandas and Python 41 | * Grasp prompt engineering to improve performance 42 | * Fine-tune LLMs and get to know the tools to unleash their power 43 | * Deploy LLMs as a service with LangChain and apply evaluation strategies 44 | * Privately interact with documents using open-source LLMs to prevent data leaks 45 | 46 | Who this book is for 47 | The book is for developers, researchers, and anyone interested in learning more about LangChain. Whether you are a beginner or an experienced developer, this book will serve as a valuable resource if you want to get the most out of LLMs using LangChain. 48 | 49 | Basic knowledge of Python is a prerequisite, while prior exposure to machine learning will help you follow along more easily. 50 | 51 | ## Contents 52 | 53 | 1. What Is Generative AI? 54 | 2. LangChain for LLM Apps 55 | 3. Getting Started with LangChain 56 | 4. Building Capable Assistants 57 | 5. Building a Chatbot like ChatGPT 58 | 6. Developing Software with Generative AI 59 | 7. LLMs for Data Science 60 | 8. Customizing LLMs and Their Output 61 | 9. Generative AI in Production 62 | 10. The Future of Generative Models 63 | -------------------------------------------------------------------------------- /books/hands-on-large-language-models.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jason2Brownlee/awesome-llm-books/7aad1f488e993fdf011149a22b2d8a7bae98b007/books/hands-on-large-language-models.jpeg -------------------------------------------------------------------------------- /books/hands-on-large-language-models.md: -------------------------------------------------------------------------------- 1 | # Hands-On Large Language Models 2 | 3 | [home](../) 4 | 5 | ![Cover Image](hands-on-large-language-models.jpeg) 6 | 7 | ## Details 8 | 9 | * **Title**: Hands-On Large Language Models 10 | * **Subtitle**: Language Understanding and Generation 11 | * **Authors**: Jay Alammar and Maarten Grootendorst 12 | * **Publication Date**: 2024 13 | * **Publisher**: O'Reilly 14 | * **ISBN-13**: 978-1098150969 15 | * **Pages**: 425 16 | * **Amazon Rating**: 4.6 stars 17 | * **Goodreads Rating**: 4.36 stars 18 | 19 | 20 | **Links**: [Amazon](https://a.co/d/hXs5jDF) | 21 | [Goodreads](https://www.goodreads.com/book/show/210408850-hands-on-large-language-models) | 22 | [Publisher](https://www.oreilly.com/library/view/hands-on-large-language/9781098150952/) | 23 | [GitHub Project](https://github.com/HandsOnLLM/Hands-On-Large-Language-Models) 24 | 25 | ## Blurb 26 | 27 | AI has acquired startling new language capabilities in just the past few years. Driven by rapid advances in deep learning, language AI systems are able to write and understand text better than ever before. This trend is enabling new features, products, and entire industries. Through this book's visually educational nature, readers will learn practical tools and concepts they need to use these capabilities today. 28 | 29 | You'll understand how to use pretrained large language models for use cases like copywriting and summarization; create semantic search systems that go beyond keyword matching; and use existing libraries and pretrained models for text classification, search, and clusterings. 30 | 31 | This book also helps you: 32 | 33 | * Understand the architecture of Transformer language models that excel at text generation and representation 34 | * Build advanced LLM pipelines to cluster text documents and explore the topics they cover 35 | * Build semantic search engines that go beyond keyword search, using methods like dense retrieval and rerankers 36 | * Explore how generative models can be used, from prompt engineering all the way to retrieval-augmented generation 37 | * Gain a deeper understanding of how to train LLMs and optimize them for specific applications using generative model fine-tuning, contrastive fine-tuning, and in-context learning 38 | 39 | ## Contents 40 | 41 | 1. Introduction to Language Models 42 | 2. Tokens and Embeddings 43 | 3. Looking Inside Transformer LLMs 44 | 4. Text Classification 45 | 5. Text Clustering and Topic Modeling 46 | 6. Prompt Engineering 47 | 7. Advanced Text Generation Techniques and Tools 48 | 8. Semantic Search and Retrieval-Augmented Generation 49 | 9. Multimodal Large Language Models 50 | 10. Creating Text Embedding Models 51 | 11. Fine-tuning Representation Models for Classification 52 | 12. Fine-tuning Generation Models 53 | -------------------------------------------------------------------------------- /books/langchain-crash-course.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jason2Brownlee/awesome-llm-books/7aad1f488e993fdf011149a22b2d8a7bae98b007/books/langchain-crash-course.jpeg -------------------------------------------------------------------------------- /books/langchain-crash-course.md: -------------------------------------------------------------------------------- 1 | # LangChain Crash Course 2 | 3 | [home](../) 4 | 5 | ![Cover Image](langchain-crash-course.jpeg) 6 | 7 | ## Details 8 | 9 | * **Title**: LangChain Crash Course 10 | * **Subtitle**: Build OpenAI LLM powered Apps: Fast track to building OpenAI LLM powered Apps using Python 11 | * **Authors**: Greg Lim 12 | * **Publication Date**: 2024 13 | * **Publisher**: Independently Published 14 | * **ISBN-13**: 978-9819411474 15 | * **Pages**: 88 16 | * **Amazon Rating**: 4.2 stars 17 | * **Goodreads Rating**: 4.07 stars 18 | 19 | 20 | **Links**: [Amazon](https://a.co/d/ibgu6jy) | 21 | [Goodreads](https://www.goodreads.com/book/show/198671257-langchain-crash-course) | 22 | [Publisher](https://greglim.gumroad.com/l/langchain) 23 | 24 | ## Blurb 25 | 26 | In this short course, we take you on a fun, hands-on and pragmatic journey to learn how to build LLM powered apps using LangChain. You'll start building your first Generative AI app within minutes. Every section is recorded in a bite-sized manner and straight to the point as I don’t want to waste your time (and most certainly mine) on the content you don't need. 27 | 28 | In this course, we will cover: 29 | 30 | * What is LangChain 31 | * How does LangChain Work 32 | * Installation, Setup and Our First LangChain App 33 | * Building a Medium Article Generator App 34 | * Connecting to OpenAI LLM 35 | * Prompt Templates 36 | * Simple Chains 37 | * Sequential Chains 38 | * Agents 39 | * Chat with a Document 40 | * Adding Memory (Chat History) 41 | * Outputting the Chat History 42 | * Uploading Custom Documents 43 | * Loading Different Document Types (eg PDF, txt, docs) 44 | * Chat with Youtube and more... 45 | 46 | The goal of this course is to teach you LangChain development in a manageable way without overwhelming you. We focus only on the essentials and cover the material in a hands-on practice manner for you to code along. 47 | 48 | Working Through This Course 49 | 50 | This course is purposely broken down into short sections where the development process of each section will center on different essential topics. The course a practical hands on approach to learning through practice. You learn best when you code along with the examples. 51 | 52 | ## Contents 53 | 54 | 1. Introduction 55 | 2. What Is Langchain 56 | 3. How Does Langchain Work 57 | 4. Installation, Setup And Our First Langchain App 58 | 5. Connecting To Openai Llm 59 | 6. Prompt Templates 60 | 7. Simple Chains 61 | 8. Sequential Chains 62 | 9. Agents 63 | 10. Chat With A Document 64 | 11. Adding Memory (Chat History) 65 | 12. Outputting The Chat History 66 | 13. Uploading Custom Documents 67 | 14. Loading Different File Types 68 | 15. Chat With Youtube 69 | -------------------------------------------------------------------------------- /books/large-language-models.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jason2Brownlee/awesome-llm-books/7aad1f488e993fdf011149a22b2d8a7bae98b007/books/large-language-models.jpeg -------------------------------------------------------------------------------- /books/large-language-models.md: -------------------------------------------------------------------------------- 1 | # Large Language Models 2 | 3 | [home](../) 4 | 5 | ![Cover Image](large-language-models.jpeg) 6 | 7 | ## Details 8 | 9 | * **Title**: Large Language Models 10 | * **Subtitle**: A Deep Dive: Bridging Theory and Practice 11 | * **Authors**: Uday Kamath, Kevin Keenan, Garrett Somers, and Sarah Sorenson 12 | * **Publication Date**: 2024 13 | * **Publisher**: Springer 14 | * **ISBN-13**: 978-3031656460 15 | * **Pages**: 506 16 | * **Amazon Rating**: 4.1 stars 17 | * **Goodreads Rating**: 4.00 stars 18 | 19 | 20 | **Links**: [Amazon](https://a.co/d/6IMNpkX) | 21 | [Goodreads](https://www.goodreads.com/book/show/214355031-large-language-models) | 22 | [Publisher](https://link.springer.com/book/10.1007/978-3-031-65647-7) | 23 | [GitHub Project](https://github.com/springer-llms-deep-dive/llms-deep-dive-tutorials) 24 | 25 | ## Blurb 26 | 27 | Large Language Models (LLMs) have emerged as a cornerstone technology, transforming how we interact with information and redefining the boundaries of artificial intelligence. LLMs offer an unprecedented ability to understand, generate, and interact with human language in an intuitive and insightful manner, leading to transformative applications across domains like content creation, chatbots, search engines, and research tools. While fascinating, the complex workings of LLMs―their intricate architecture, underlying algorithms, and ethical considerations―require thorough exploration, creating a need for a comprehensive book on this subject. 28 | 29 | This book provides an authoritative exploration of the design, training, evolution, and application of LLMs. It begins with an overview of pre-trained language models and Transformer architectures, laying the groundwork for understanding prompt-based learning techniques. Next, it dives into methods for fine-tuning LLMs, integrating reinforcement learning for value alignment, and the convergence of LLMs with computer vision, robotics, and speech processing. The book strongly emphasizes practical applications, detailing real-world use cases such as conversational chatbots, retrieval-augmented generation (RAG), and code generation. These examples are carefully chosen to illustrate the diverse and impactful ways LLMs are being applied in various industries and scenarios. 30 | 31 | Readers will gain insights into operationalizing and deploying LLMs, from implementing modern tools and libraries to addressing challenges like bias and ethical implications. The book also introduces the cutting-edge realm of multimodal LLMs that can process audio, images, video, and robotic inputs. With hands-on tutorials for applying LLMs to natural language tasks, this thorough guide equips readers with both theoretical knowledge and practical skills for leveraging the full potential of large language models. 32 | 33 | This comprehensive resource is appropriate for a wide audience: students, researchers and academics in AI or NLP, practicing data scientists, and anyone looking to grasp the essence and intricacies of LLMs. 34 | 35 | Key Features: 36 | 37 | * Over 100 techniques and state-of-the-art methods, including pre-training, prompt-based tuning, instruction tuning, parameter-efficient and compute-efficient fine-tuning, end-user prompt engineering, and building and optimizing Retrieval-Augmented Generation systems, along with strategies for aligning LLMs with human values using reinforcement learning 38 | * Over 200 datasets compiled in one place, covering everything from pre- training to multimodal tuning, providing a robust foundation for diverse LLM applications 39 | * Over 50 strategies to address key ethical issues such as hallucination, toxicity, bias, fairness, and privacy. Gain comprehensive methods for measuring, evaluating, and mitigating these challenges to ensure responsible LLM deployment 40 | * Over 200 benchmarks covering LLM performance across various tasks, ethical considerations, multimodal applications, and more than 50 evaluation metrics for the LLM lifecycle 41 | * Nine detailed tutorials that guide readers through pre-training, fine- tuning, alignment tuning, bias mitigation, multimodal training, and deploying large language models using tools and libraries compatible with Google Colab, ensuring practical application of theoretical concepts 42 | * Over 100 practical tips for data scientists and practitioners, offering implementation details, tricks, and tools to successfully navigate the LLM life- cycle and accomplish tasks efficiently 43 | 44 | ## Contents 45 | 46 | 1. Large Language Models: An Introduction 47 | 2. Language Models Pre-training 48 | 3. Prompt-based Learning 49 | 4. LLM Adaptation and Utilization 50 | 5. Tuning for LLM Alignment 51 | 6. LLM Challenges and Solutions 52 | 7. Retrieval-Augmented Generation 53 | 8. LLMs in Production 54 | 9. Multimodal LLMs 55 | 10. LLMs: Evolution and New Frontiers 56 | -------------------------------------------------------------------------------- /books/llm-engineer's-handbook.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jason2Brownlee/awesome-llm-books/7aad1f488e993fdf011149a22b2d8a7bae98b007/books/llm-engineer's-handbook.jpeg -------------------------------------------------------------------------------- /books/llm-engineer's-handbook.md: -------------------------------------------------------------------------------- 1 | # LLM Engineer's Handbook 2 | 3 | [home](../) 4 | 5 | ![Cover Image](llm-engineer's-handbook.jpeg) 6 | 7 | ## Details 8 | 9 | * **Title**: LLM Engineer's Handbook 10 | * **Subtitle**: Master the art of engineering large language models from concept to production 11 | * **Authors**: Paul Iusztin and Maxime Labonne 12 | * **Publication Date**: 2024 13 | * **Publisher**: Packt 14 | * **ISBN-13**: 978-1836200079 15 | * **Amazon Rating**: 4.6 stars 16 | * **Goodreads Rating**: 3.54 stars 17 | 18 | 19 | **Links**: [Amazon](https://a.co/d/5H3ufht) | 20 | [Goodreads](https://www.goodreads.com/book/show/216193554-llm-engineer-s-handbook) | 21 | [Publisher](https://www.packtpub.com/en-au/product/llm-engineers-handbook-9781836200062) | 22 | [GitHub Project](https://github.com/PacktPublishing/LLM-Engineers-Handbook) 23 | 24 | ## Blurb 25 | 26 | Step into the world of LLMs with this practical guide that takes you from the fundamentals to deploying advanced applications using LLMOps best practices 27 | 28 | Purchase of the print or Kindle book includes a free eBook in PDF format 29 | 30 | “This book is instrumental in making sure that as many people as possible can not only use LLMs but also adapt them, fine-tune them, quantize them, and make them efficient enough to deploy in the real world.”- Julien Chaumond, CTO and Co-founder, Hugging Face 31 | 32 | Book Description 33 | This LLM book provides practical insights into designing, training, and deploying LLMs in real-world scenarios by leveraging MLOps' best practices. The guide walks you through building an LLM-powered twin that’s cost-effective, scalable, and modular. It moves beyond isolated Jupyter Notebooks, focusing on how to build production-grade end-to-end LLM systems. 34 | 35 | Throughout this book, you will learn data engineering, supervised fine-tuning, and deployment. The hands-on approach to building the LLM twin use case will help you implement MLOps components in your own projects. You will also explore cutting-edge advancements in the field, including inference optimization, preference alignment, and real-time data processing, making this a vital resource for those looking to apply LLMs in their projects. 36 | 37 | What you will learn 38 | * Implement robust data pipelines and manage LLM training cycles 39 | * Create your own LLM and refine with the help of hands-on examples 40 | * Get started with LLMOps by diving into core MLOps principles like IaC 41 | * Perform supervised fine-tuning and LLM evaluation 42 | * Deploy end-to-end LLM solutions using AWS and other tools 43 | * Explore continuous training, monitoring, and logic automation 44 | * Learn about RAG ingestion as well as inference and feature pipelines 45 | 46 | Who this book is for 47 | This book is for AI engineers, NLP professionals, and LLM engineers looking to deepen their understanding of LLMs. Basic knowledge of LLMs and the Gen AI landscape, Python and AWS is recommended. Whether you are new to AI or looking to enhance your skills, this book provides comprehensive guidance on implementing LLMs in real-world scenarios. 48 | 49 | ## Contents 50 | 51 | 1. Undersstanding the LLM Twin Concept and Architecture 52 | 2. Tooling and Installation 53 | 3. Data Engineering 54 | 4. RAG Feature Pipeline 55 | 5. Supervised Fine-tuning 56 | 6. Fine-tuning with Preference Alignment 57 | 7. Evaluating LLMs 58 | 8. Inference Optimization 59 | 9. RAG Inference Pipeline 60 | 10. Inference Pipeline Deployment 61 | 11. MLOps and LLMOps 62 | 12. MLOps Principles 63 | -------------------------------------------------------------------------------- /books/llms-in-production.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jason2Brownlee/awesome-llm-books/7aad1f488e993fdf011149a22b2d8a7bae98b007/books/llms-in-production.jpeg -------------------------------------------------------------------------------- /books/llms-in-production.md: -------------------------------------------------------------------------------- 1 | # LLMs in Production 2 | 3 | [home](../) 4 | 5 | ![Cover Image](llms-in-production.jpeg) 6 | 7 | ## Details 8 | 9 | * **Title**: LLMs in Production 10 | * **Subtitle**: From language models to successful products 11 | * **Authors**: Christopher Brousseau and Matthew Sharp 12 | * **Publication Date**: 2025 13 | * **Publisher**: Manning 14 | * **ISBN-13**: 978-1633437203 15 | * **Pages**: 456 16 | * **Amazon Rating**: 4.4 stars 17 | * **Goodreads Rating**: 4.08 stars 18 | 19 | 20 | **Links**: [Amazon](https://a.co/d/gF1w56V) | 21 | [Goodreads](https://www.goodreads.com/book/show/215144443-llms-in-production) | 22 | [Publisher](https://www.manning.com/books/llms-in-production) | 23 | [GitHub Project](https://github.com/IMJONEZZ/LLMs-in-Production) 24 | 25 | ## Blurb 26 | 27 | Learn how to put Large Language Model-based applications into production safely and efficiently. 28 | 29 | Large Language Models (LLMs) are the foundation of AI tools like ChatGPT, LLAMA and Bard. This practical book offers clear, example-rich explanations of how LLMs work, how you can interact with them, and how to integrate LLMs into your own applications. In LLMs in Production you will: 30 | 31 | * Grasp the fundamentals of LLMs and the technology behind them 32 | * Evaluate when to use a premade LLM and when to build your own 33 | * Efficiently scale up an ML platform to handle the needs of LLMs 34 | * Train LLM foundation models and finetune an existing LLM 35 | * Deploy LLMs to the cloud and edge devices using complex architectures like RLHF 36 | * Build applications leveraging the strengths of LLMs while mitigating their weaknesses 37 | 38 | LLMs in Production delivers vital insights into delivering MLOps for LLMs. You’ll learn how to operationalize these powerful AI models for chatbots, coding assistants, and more. Find out what makes LLMs so different from traditional software and ML, discover best practices for working with them out of the lab, and dodge common pitfalls with experienced advice. 39 | about the book 40 | LLMs in Production is the comprehensive guide to LLMs you’ll need to effectively guide you to production usage. It takes you through the entire lifecycle of an LLM, from initial concept, to creation and fine tuning, all the way to deployment. You’ll discover how to effectively prepare an LLM dataset, cost-efficient training techniques like LORA and RLHF, and how to evaluate your models against industry benchmarks. 41 | 42 | Learn to properly establish deployment infrastructure and address common challenges like retraining and load testing. Finally, you’ll go hands-on with three exciting example projects: a cloud-based LLM chatbot, a Code Completion VSCode Extension, and deploying LLM to edge devices like Raspberry Pi. By the time you’re done reading, you’ll be ready to start developing LLMs and effectively incorporating them into software. 43 | 44 | ## Contents 45 | 46 | 1. Word’s awakening: Why large language models have captured attention 47 | 2. Large language models: A deep dive into language modeling 48 | 3. Large language model operations: Building a platform for LLMs 49 | 4. Data engineering for large language models: Setting up for success 50 | 5. Training large language models: How to generate the generator 51 | 6. Large language model services: A practical guide 52 | 7. Prompt engineering: Becoming an LLM whisperer 53 | 8. Large Language model applications: Building an interactive experience 54 | 9. Creating an LLM project: Reimplementing Llama 3 55 | 10. Creating a coding copilot project: This would have helped you earlier 56 | 11. Deploying an LLM on a Raspberry PI: How low can you go? 57 | 12. Production, an ever-changing landscape: Things are just getting started 58 | 59 | Appendices 60 | * Appendix A: History of linguistics 61 | * Appendix B: Reinforcement learning with human feedback 62 | * Appendix C: Multimodal latent spaces 63 | -------------------------------------------------------------------------------- /books/natural-language-processing-with-transformers.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jason2Brownlee/awesome-llm-books/7aad1f488e993fdf011149a22b2d8a7bae98b007/books/natural-language-processing-with-transformers.jpeg -------------------------------------------------------------------------------- /books/natural-language-processing-with-transformers.md: -------------------------------------------------------------------------------- 1 | # Natural Language Processing with Transformers 2 | 3 | [home](../) 4 | 5 | ![Cover Image](natural-language-processing-with-transformers.jpeg) 6 | 7 | ## Details 8 | 9 | * **Title**: Natural Language Processing with Transformers 10 | * **Subtitle**: Building Language Applications with Hugging Face 11 | * **Authors**: Lewis Tunstall, Leandro von Werra and Thomas Wolf 12 | * **Publication Date**: 2022 13 | * **Publisher**: O'Reilly 14 | * **ISBN-13**: 978-9355420329 15 | * **Pages**: 406 16 | * **Amazon Rating**: 4.6 stars 17 | * **Goodreads Rating**: 4.41 stars 18 | 19 | 20 | **Links**: [Amazon](https://a.co/d/5WIiVAC) | 21 | [Goodreads](https://www.goodreads.com/book/show/60114857-natural-language-processing-with-transformers) | 22 | [Publisher](https://www.oreilly.com/library/view/natural-language-processing/9781098136789/) | 23 | [GitHub Project](https://github.com/nlp-with-transformers/notebooks) 24 | 25 | ## Blurb 26 | 27 | Since their introduction in 2017, transformers have quickly become the dominant architecture for achieving state-of-the-art results on a variety of natural language processing tasks. If you're a data scientist or coder, this practical book shows you how to train and scale these large models using Hugging Face Transformers, a Python-based deep learning library. 28 | 29 | Transformers have been used to write realistic news stories, improve Google Search queries, and even create chatbots that tell corny jokes. In this guide, authors Lewis Tunstall, Leandro von Werra, and Thomas Wolf, among the creators of Hugging Face Transformers, use a hands-on approach to teach you how transformers work and how to integrate them in your applications. You'll quickly learn a variety of tasks they can help you solve. 30 | 31 | * Build, debug, and optimize transformer models for core NLP tasks, such as text classification, named entity recognition, and question answering 32 | * Learn how transformers can be used for cross-lingual transfer learning 33 | * Apply transformers in real-world scenarios where labeled data is scarce 34 | * Make transformer models efficient for deployment using techniques such as distillation, pruning, and quantization 35 | * Train transformers from scratch and learn how to scale to multiple GPUs and distributed environments 36 | 37 | ## Contents 38 | 39 | 1. Introduction 40 | 2. Text Classification 41 | 3. Transformer Anatomy 42 | 4. Multilingual Named Entity Recognition 43 | 5. Text Generation 44 | 6. Summarization 45 | 7. Question Answering 46 | 8. Making Transformers Efficient in Production 47 | 9. Dealing with Few to No Labels 48 | 10. Training Transformers from Scratch 49 | 11. Future Directions 50 | -------------------------------------------------------------------------------- /books/prompt-engineering-for-generative-ai.md: -------------------------------------------------------------------------------- 1 | # Prompt Engineering for Generative AI 2 | 3 | [home](../) 4 | 5 | ![Cover Image](prompt-engineering-for-generative-ai.png) 6 | 7 | ## Details 8 | 9 | * **Title**: Prompt Engineering for Generative AI 10 | * **Subtitle**: Future-Proof Inputs for Reliable AI Outputs 11 | * **Authors**: James Phoenix and Mike Taylor 12 | * **Publication Date**: 2024 13 | * **Publisher**: O'Reilly 14 | * **ISBN-13**: 978-1098153434 15 | * **Pages**: 422 16 | * **Amazon Rating**: 4.5 stars 17 | * **Goodreads Rating**: 3.56 stars 18 | 19 | 20 | **Links**: [Amazon](https://a.co/d/52xLb9K) | 21 | [Goodreads](https://www.goodreads.com/book/show/204133880-prompt-engineering-for-generative-ai) | 22 | [Publisher](https://www.oreilly.com/library/view/prompt-engineering-for/9781098153427/) | 23 | [GitHub Project](https://github.com/BrightPool/prompt-engineering-for-generative-ai-examples) 24 | 25 | ## Blurb 26 | 27 | Large language models (LLMs) and diffusion models such as ChatGPT and Stable Diffusion have unprecedented potential. Because they have been trained on all the public text and images on the internet, they can make useful contributions to a wide variety of tasks. And with the barrier to entry greatly reduced today, practically any developer can harness LLMs and diffusion models to tackle problems previously unsuitable for automation. 28 | 29 | With this book, you'll gain a solid foundation in generative AI, including how to apply these models in practice. When first integrating LLMs and diffusion models into their workflows, most developers struggle to coax reliable enough results from them to use in automated systems. Authors James Phoenix and Mike Taylor show you how a set of principles called prompt engineering can enable you to work effectively with AI. 30 | 31 | Learn how to empower AI to work for you. This book explains: 32 | 33 | * The structure of the interaction chain of your program's AI model and the fine-grained steps in between 34 | * How AI model requests arise from transforming the application problem into a document completion problem in the model training domain 35 | * The influence of LLM and diffusion model architecture—and how to best interact with it 36 | * How these principles apply in practice in the domains of natural language processing, text and image generation, and code 37 | 38 | ## Contents 39 | 40 | 1. Five Pillars of Prompting 41 | 2. Intro to Text Generation Models 42 | 3. Standard Practices for Text Generation 43 | 4. Advanced Techniques for Text Generation with Langchain 44 | 5. Vector Databases 45 | 6. Autonomous Agents with Memory and Tools 46 | 7. Intro to Diffusion Models for Image Generation 47 | 8. Standard Practices for Image Generation 48 | 9. Advanced Techniques for Image Generation 49 | 10. Building AI-powered Applications 50 | -------------------------------------------------------------------------------- /books/prompt-engineering-for-generative-ai.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jason2Brownlee/awesome-llm-books/7aad1f488e993fdf011149a22b2d8a7bae98b007/books/prompt-engineering-for-generative-ai.png -------------------------------------------------------------------------------- /books/prompt-engineering-for-llms.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jason2Brownlee/awesome-llm-books/7aad1f488e993fdf011149a22b2d8a7bae98b007/books/prompt-engineering-for-llms.jpeg -------------------------------------------------------------------------------- /books/prompt-engineering-for-llms.md: -------------------------------------------------------------------------------- 1 | # Prompt Engineering for LLMs 2 | 3 | [home](../) 4 | 5 | ![Cover Image](prompt-engineering-for-llms.jpeg) 6 | 7 | ## Details 8 | 9 | * **Title**: Prompt Engineering for LLMs 10 | * **Subtitle**: The Art and Science of Building Large Language Model–Based Applications 11 | * **Authors**: John Berryman and Albert Ziegler 12 | * **Publication Date**: 2024 13 | * **Publisher**: O'Reilly 14 | * **ISBN-13**: 978-1098156152 15 | * **Pages**: 280 16 | * **Amazon Rating**: 4.4 stars 17 | * **Goodreads Rating**: 4.00 stars 18 | 19 | 20 | **Links**: [Amazon](https://a.co/d/eyWEQ4A) | 21 | [Goodreads](https://www.goodreads.com/book/show/213739653-prompt-engineering-for-llms) | 22 | [Publisher](https://www.oreilly.com/library/view/prompt-engineering-for/9781098156145/) 23 | 24 | ## Blurb 25 | 26 | Large language models (LLMs) are revolutionizing the world, promising to automate tasks and solve complex problems. A new generation of software applications are using these models as building blocks to unlock new potential in almost every domain, but reliably accessing these capabilities requires new skills. This book will teach you the art and science of prompt engineering-the key to unlocking the true potential of LLMs. 27 | 28 | Industry experts John Berryman and Albert Ziegler share how to communicate effectively with AI, transforming your ideas into a language model-friendly format. By learning both the philosophical foundation and practical techniques, you'll be equipped with the knowledge and confidence to build the next generation of LLM-powered applications. 29 | 30 | * Understand LLM architecture and learn how to best interact with it 31 | * Design a complete prompt-crafting strategy for an application 32 | * Gather, triage, and present context elements to make an efficient prompt 33 | * Master specific prompt-crafting techniques like few-shot learning, chain-of-thought prompting, and RAG 34 | 35 | ## Contents 36 | 37 | 1. Introduction to Prompt Engineering 38 | 2. Understanding LLMs 39 | 3. Moving to Chat 40 | 4. Designing LLM Applications 41 | 5. Prompt Content 42 | 6. Assembling the Prompt 43 | 7. Taming the Model 44 | 8. Conversational Agency 45 | 9. LLM Workflows 46 | 10. Evaluating LLM Applications 47 | 11. Looking Ahead 48 | -------------------------------------------------------------------------------- /books/quick-start-guide-to-large-language-models.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jason2Brownlee/awesome-llm-books/7aad1f488e993fdf011149a22b2d8a7bae98b007/books/quick-start-guide-to-large-language-models.jpeg -------------------------------------------------------------------------------- /books/quick-start-guide-to-large-language-models.md: -------------------------------------------------------------------------------- 1 | # Quick Start Guide to Large Language Models 2 | 3 | [home](../) 4 | 5 | ![Cover Image](quick-start-guide-to-large-language-models.jpeg) 6 | 7 | ## Details 8 | 9 | * **Title**: Quick Start Guide to Large Language Models 10 | * **Subtitle**: Strategies and Best Practices for ChatGPT, Embeddings, Fine-Tuning, and Multimodal AI 11 | * **Authors**: Sinan Ozdemir 12 | * **Publication Date**: 2024 13 | * **Publisher**: Addison-Wesley 14 | * **ISBN-13**: 978-0135346563 15 | * **Pages**: 384 16 | * **Amazon Rating**: 4.9 stars 17 | * **Goodreads Rating**: 3.64 stars 18 | 19 | 20 | **Links**: [Amazon](https://a.co/d/aUsDJ7e) | 21 | [Goodreads](https://www.goodreads.com/book/show/126850297-quick-start-guide-to-large-language-models) | 22 | [Publisher](https://www.pearson.com/en-us/subject-catalog/p/quick-start-guide-to-large-language-models-2nd-edition/P200000012793) | 23 | [GitHub Project](https://github.com/sinanuozdemir/quick-start-guide-to-llms) 24 | 25 | ## Blurb 26 | 27 | The Practical, Step-by-Step Guide to Using LLMs at Scale in Projects and Products 28 | 29 | Large Language Models (LLMs) like Llama 3, Claude 3, and the GPT family are demonstrating breathtaking capabilities, but their size and complexity have deterred many practitioners from applying them. In Quick Start Guide to Large Language Models, Second Edition, pioneering data scientist and AI entrepreneur Sinan Ozdemir clears away those obstacles and provides a guide to working with, integrating, and deploying LLMs to solve practical problems. 30 | 31 | Ozdemir brings together all you need to get started, even if you have no direct experience with LLMs: step-by-step instructions, best practices, real-world case studies, and hands-on exercises. Along the way, he shares insights into LLMs' inner workings to help you optimize model choice, data formats, prompting, fine-tuning, performance, and much more. The resources on the companion website include sample datasets and up-to-date code for working with open- and closed-source LLMs such as those from OpenAI (GPT-4 and GPT-3.5), Google (BERT, T5, and Gemini), X (Grok), Anthropic (the Claude family), Cohere (the Command family), and Meta (BART and the LLaMA family). 32 | 33 | * Learn key concepts: pre-training, transfer learning, fine-tuning, attention, embeddings, tokenization, and more 34 | Use APIs and Python to fine-tune and customize LLMs for your requirements 35 | * Build a complete neural/semantic information retrieval system and attach to conversational LLMs for building retrieval-augmented generation (RAG) chatbots and AI Agents 36 | * Master advanced prompt engineering techniques like output structuring, chain-of-thought prompting, and semantic few-shot prompting 37 | * Customize LLM embeddings to build a complete recommendation engine from scratch with user data that outperforms out-of-the-box embeddings from OpenAI 38 | * Construct and fine-tune multimodal Transformer architectures from scratch using open-source LLMs and large visual datasets 39 | * Align LLMs using Reinforcement Learning from Human and AI Feedback (RLHF/RLAIF) to build conversational agents from open models like Llama 3 and FLAN-T5 40 | * Deploy prompts and custom fine-tuned LLMs to the cloud with scalability and evaluation pipelines in mind 41 | * Diagnose and optimize LLMs for speed, memory, and performance with quantization, probing, benchmarking, and evaluation frameworks 42 | 43 | ## Contents 44 | 45 | Part I - Introduction to Large Language Models 46 | * Chapter 2: Semantic Search with LLMs 47 | * Chapter 3: First Steps with Prompt Engineering 48 | * Chapter 4: The AI Ecosystem: Putting the Pieces Together 49 | 50 | Part II - Getting the Most Out of LLMs 51 | * Chapter 5: Optimizing LLMs with Customized Fine-Tuning 52 | * Chapter 6: Advanced Prompt Engineering 53 | * Chapter 7: Customizing Embeddings and Model Architectures 54 | 55 | Part III - Advanced LLM Usage 56 | * Chapter 9: Moving Beyond Foundation Models 57 | * Chapter 10: Advanced Open-Source LLM Fine-Tuning 58 | * Chapter 11: Moving LLMs into Production 59 | * Chapter 12: Evaluating LLMs 60 | -------------------------------------------------------------------------------- /books/rag-driven-generative-ai.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jason2Brownlee/awesome-llm-books/7aad1f488e993fdf011149a22b2d8a7bae98b007/books/rag-driven-generative-ai.jpeg -------------------------------------------------------------------------------- /books/rag-driven-generative-ai.md: -------------------------------------------------------------------------------- 1 | # RAG-Driven Generative AI 2 | 3 | [home](../) 4 | 5 | ![Cover Image](rag-driven-generative-ai.jpeg) 6 | 7 | ## Details 8 | 9 | * **Title**: RAG-Driven Generative AI 10 | * **Subtitle**: Build custom retrieval augmented generation pipelines with LlamaIndex, Deep Lake, and Pinecone 11 | * **Authors**: Denis Rothman 12 | * **Publication Date**: 2024 13 | * **Publisher**: Packt 14 | * **ISBN-13**: 978-1836200918 15 | * **Pages**: 334 16 | * **Amazon Rating**: 4 stars 17 | * **Goodreads Rating**: 3.90 stars 18 | 19 | 20 | **Links**: [Amazon](https://a.co/d/2zjaDK4) | 21 | [Goodreads](https://www.goodreads.com/book/show/214330235-rag-driven-generative-ai) | 22 | [Publisher](https://www.packtpub.com/en-us/product/rag-driven-generative-ai-9781836200918) | 23 | [GitHub Project](https://github.com/Denis2054/RAG-Driven-Generative-AI) 24 | 25 | ## Blurb 26 | 27 | Minimize AI hallucinations and build accurate, custom generative AI pipelines with RAG using embedded vector databases and integrated human feedback 28 | 29 | Purchase of the print or Kindle book includes a free eBook in PDF format 30 | 31 | Key Features 32 | * Implement RAG’s traceable outputs, linking each response to its source document to build reliable multimodal conversational agents 33 | * Deliver accurate generative AI models in pipelines integrating RAG, real-time human feedback improvements, and knowledge graphs 34 | * Balance cost and performance between dynamic retrieval datasets and fine-tuning static data 35 | 36 | Book Description 37 | RAG-Driven Generative AI provides a roadmap for building effective LLM, computer vision, and generative AI systems that balance performance and costs. 38 | 39 | This book offers a detailed exploration of RAG and how to design, manage, and control multimodal AI pipelines. By connecting outputs to traceable source documents, RAG improves output accuracy and contextual relevance, offering a dynamic approach to managing large volumes of information. This AI book shows you how to build a RAG framework, providing practical knowledge on vector stores, chunking, indexing, and ranking. You’ll discover techniques to optimize your project’s performance and better understand your data, including using adaptive RAG and human feedback to refine retrieval accuracy, balancing RAG with fine-tuning, implementing dynamic RAG to enhance real-time decision-making, and visualizing complex data with knowledge graphs. 40 | 41 | You’ll be exposed to a hands-on blend of frameworks like LlamaIndex and Deep Lake, vector databases such as Pinecone and Chroma, and models from Hugging Face and OpenAI. By the end of this book, you will have acquired the skills to implement intelligent solutions, keeping you competitive in fields from production to customer service across any project. 42 | 43 | What you will learn 44 | * Scale RAG pipelines to handle large datasets efficiently 45 | * Employ techniques that minimize hallucinations and ensure accurate responses 46 | * Implement indexing techniques to improve AI accuracy with traceable and transparent outputs 47 | * Customize and scale RAG-driven generative AI systems across domains 48 | * Find out how to use Deep Lake and Pinecone for efficient and fast data retrieval 49 | * Control and build robust generative AI systems grounded in real-world data 50 | * Combine text and image data for richer, more informative AI responses 51 | 52 | Who this book is for 53 | This book is ideal for data scientists, AI engineers, machine learning engineers, and MLOps engineers. If you are a solutions architect, software developer, product manager, or project manager looking to enhance the decision-making process of building RAG applications, then you’ll find this book useful. 54 | 55 | ## Contents 56 | 57 | 1. Why Retrieval Augmented Generation? 58 | 2. RAG Embedding Vector Stores with Deep Lake and OpenAI 59 | 3. Building Index-Based RAG with LlamaIndex, Deep Lake, and OpenAI 60 | 4. Multimodal Modular RAG for Drone Technology 61 | 5. Boosting RAG Performance with Expert Human Feedback 62 | 6. Scaling RAG Bank Customer Data with Pinecone 63 | 7. Building Scalable Knowledge-Graph-Based RAG with Wikipedia API and LlamaIndex 64 | 8. Dynamic RAG with Chroma and Hugging Face Llama 65 | 9. Empowering AI Models: Fine-Tuning RAG Data and Human Feedback 66 | 10. RAG for Video Stock Production with Pinecone and OpenAI 67 | -------------------------------------------------------------------------------- /books/super-study-guide.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jason2Brownlee/awesome-llm-books/7aad1f488e993fdf011149a22b2d8a7bae98b007/books/super-study-guide.jpeg -------------------------------------------------------------------------------- /books/super-study-guide.md: -------------------------------------------------------------------------------- 1 | # Super Study Guide 2 | 3 | [home](../) 4 | 5 | ![Cover Image](super-study-guide.jpeg) 6 | 7 | ## Details 8 | 9 | * **Title**: Super Study Guide 10 | * **Subtitle**: Transformers & Large Language Models 11 | * **Authors**: Afshine Amidi and Shervine Amidi 12 | * **Publication Date**: 2024 13 | * **Publisher**: Independently published 14 | * **ISBN-13**: 979-8836693312 15 | * **Pages**: 247 16 | * **Amazon Rating**: 4.6 stars 17 | * **Goodreads Rating**: 4.58 stars 18 | 19 | 20 | **Links**: [Amazon](https://a.co/d/aE3pz72) | 21 | [Goodreads](https://www.goodreads.com/book/show/217141763-super-study-guide) | 22 | [Publisher](https://superstudy.guide/transformers-large-language-models/) 23 | 24 | ## Blurb 25 | 26 | This book is a concise and illustrated guide for anyone who wants to understand the inner workings of large language models in the context of interviews, projects or to satisfy their own curiosity. 27 | 28 | It is divided into 5 parts: 29 | 30 | * Foundations: primer on neural networks and important deep learning concepts for training and evaluation 31 | * Embeddings: tokenization algorithms, word-embeddings (word2vec) and sentence embeddings (RNN, LSTM, GRU) 32 | * Transformers: motivation behind its self-attention mechanism, detailed overview on the encoder-decoder architecture and related variations such as BERT, GPT and T5, along with tips and tricks on how to speed up computations 33 | * Large language models: main techniques to tune Transformer-based models, such as prompt engineering, (parameter efficient) finetuning and preference tuning 34 | * Applications: most common problems including sentiment extraction, machine translation, retrieval-augmented generation and many more 35 | 36 | ## Contents 37 | 38 | 1. Foundations 39 | 2. Embeddings 40 | 3. Transformers 41 | 4. Large language models 42 | 5. Applications 43 | -------------------------------------------------------------------------------- /books/the-developer's-playbook-for-large-language-model-security.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jason2Brownlee/awesome-llm-books/7aad1f488e993fdf011149a22b2d8a7bae98b007/books/the-developer's-playbook-for-large-language-model-security.jpeg -------------------------------------------------------------------------------- /books/the-developer's-playbook-for-large-language-model-security.md: -------------------------------------------------------------------------------- 1 | # The Developer's Playbook for Large Language Model Security 2 | 3 | [home](../) 4 | 5 | ![Cover Image](the-developer's-playbook-for-large-language-model-security.jpeg) 6 | 7 | ## Details 8 | 9 | * **Title**: The Developer's Playbook for Large Language Model Security 10 | * **Subtitle**: Building Secure AI Applications 11 | * **Authors**: Steve Wilson 12 | * **Publication Date**: 2024 13 | * **Publisher**: O'Reilly 14 | * **ISBN-13**: 978-1098162207 15 | * **Pages**: 200 16 | * **Amazon Rating**: 5 stars 17 | * **Goodreads Rating**: 3.88 stars 18 | 19 | 20 | **Links**: [Amazon](https://a.co/d/d3rJVkn) | 21 | [Goodreads](https://www.goodreads.com/book/show/210408897-the-developer-s-playbook-for-large-language-model-security) | 22 | [Publisher](https://www.oreilly.com/library/view/the-developers-playbook/9781098162191/) 23 | 24 | ## Blurb 25 | 26 | Large language models (LLMs) are not just shaping the trajectory of AI, they're also unveiling a new era of security challenges. This practical book takes you straight to the heart of these threats. Author Steve Wilson, chief product officer at Exabeam, focuses exclusively on LLMs, eschewing generalized AI security to delve into the unique characteristics and vulnerabilities inherent in these models. 27 | 28 | Complete with collective wisdom gained from the creation of the OWASP Top 10 for LLMs list—a feat accomplished by more than 400 industry experts—this guide delivers real-world guidance and practical strategies to help developers and security teams grapple with the realities of LLM applications. Whether you're architecting a new application or adding AI features to an existing one, this book is your go-to resource for mastering the security landscape of the next frontier in AI. 29 | 30 | You'll learn: 31 | 32 | * Why LLMs present unique security challenges 33 | * How to navigate the many risk conditions associated with using LLM technology 34 | * The threat landscape pertaining to LLMs and the critical trust boundaries that must be maintained 35 | * How to identify the top risks and vulnerabilities associated with LLMs 36 | * Methods for deploying defenses to protect against attacks on top vulnerabilities 37 | * Ways to actively manage critical trust boundaries on your systems to ensure secure execution and risk minimization 38 | 39 | ## Contents 40 | 41 | 1. Chatbots Breaking Bad 42 | 2. The OWASP Top 10 for LLM Applications 43 | 3. Architectures and Trust Boundaries 44 | 4. Prompt Injection 45 | 5. Can Your LLM Know Too Much? 46 | 6. Do Language Models Dream of Electric Sheep? 47 | 7. Trust No One 48 | 8. Don’t Lose Your Wallet 49 | 9. Find the Weakest Link 50 | 10. Learning from Future History 51 | 11. Trust the Process 52 | 12. A Practical Framework for Responsible AI Security 53 | -------------------------------------------------------------------------------- /books/what-is-chatgpt-doing....jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jason2Brownlee/awesome-llm-books/7aad1f488e993fdf011149a22b2d8a7bae98b007/books/what-is-chatgpt-doing....jpeg -------------------------------------------------------------------------------- /books/what-is-chatgpt-doing....md: -------------------------------------------------------------------------------- 1 | # What Is ChatGPT Doing... 2 | 3 | [home](../) 4 | 5 | ![Cover Image](what-is-chatgpt-doing....jpeg) 6 | 7 | ## Details 8 | 9 | * **Title**: What Is ChatGPT Doing... 10 | * **Subtitle**: ...and Why Does It Work? 11 | * **Authors**: Stephen Wolfram 12 | * **Publication Date**: 2023 13 | * **Publisher**: Wolfram Media Inc. 14 | * **ISBN-13**: 978-1579550813 15 | * **Pages**: 102 16 | * **Amazon Rating**: 4.2 stars 17 | * **Goodreads Rating**: 3.86 stars 18 | 19 | 20 | **Links**: [Amazon](https://a.co/d/79xVzR5) | 21 | [Goodreads](https://www.goodreads.com/book/show/123451665-what-is-chatgpt-doing-and-why-does-it-work) | 22 | [Publisher](https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-doing-and-why-does-it-work/) 23 | 24 | ## Blurb 25 | 26 | Nobody expected this—not even its creators: ChatGPT has burst onto the scene as an AI capable of writing at a convincingly human level. But how does it really work? What's going on inside its "AI mind"? In this short book, prominent scientist and computation pioneer Stephen Wolfram provides a readable and engaging explanation that draws on his decades-long unique experience at the frontiers of science and technology. Find out how the success of ChatGPT brings together the latest neural net technology with foundational questions about language and human thought posed by Aristotle more than two thousand years ago. 27 | 28 | ## Contents 29 | 30 | 1. It's Just Adding One Word at a Time 31 | 2. Where Do the Probabilities Come From? 32 | 3. What Is a Model? 33 | 4. Models for Human-Like Tasks 34 | 5. Neural Nets 35 | 6. Machine Learning, and the Training of Neural Nets 36 | 7. The Practice and Lore of Neural Net Training 37 | 8. "Surely a Network That’s Big Enough Can Do Anything!"" 38 | 9. The Concept of Embeddings 39 | 10. Inside ChatGPT 40 | 11. The Training of ChatGPT 41 | 12. Beyond Basic Training 42 | 13. What Really Lets ChatGPT Work? 43 | 14. Meaning Space and Semantic Laws of Motion 44 | 15. Semantic Grammar and the Power of Computational Language 45 | 16. So ... What Is ChatGPT Doing, and Why Does It Work? 46 | --------------------------------------------------------------------------------