└── README.md


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 1 | # Mosaic AI Agent Framework & Agent Evaluation
 2 | 
 3 | The content from this repository has been moved to the [Databricks Generative AI Cookbook](http://ai-cookbook.io) which is accompanied by [production-ready code](https://github.com/databricks/genai-cookbook/tree/main/rag_app_sample_code) to build a **high-quality RAG application** using Databricks.
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 5 | 
 6 | The [Mosaic Generative AI Cookbook](https://ai-cookbook.io/) provides:
 7 |   - A conceptual overview and deep dive into various Generative AI design patterns, such as Prompt Engineering, Agents, RAG, and Fine Tuning
 8 |   - An overview of Evaluation-Driven development
 9 |   - The theory of every parameter/knob that impacts quality
10 |   - How to root cause quality issues and detemermine which knobs are relevant to experiment with for your use case
11 |   - Best practices for how to experiment with each knob
12 | 
13 | The [provided code](https://github.com/databricks/genai-cookbook/tree/main/rag_app_sample_code) is intended for use with the Databricks platform.  Specifically:
14 | - [Mosaic AI Agent Framework](https://docs.databricks.com/en/generative-ai/retrieval-augmented-generation.html) which provides a fast developer workflow with enterprise-ready LLMops & governance
15 | - [Mosaic AI Agent Evaluation](https://docs.databricks.com/en/generative-ai/agent-evaluation/index.html) which provides reliable, quality measurement using proprietary AI-assisted LLM judges to measure quality metrics that are powered by human feedback collected through an intuitive web-based chat UI
16 | 
17 | ![Alt text](https://raw.githubusercontent.com/databricks/genai-cookbook/main/rag_app_sample_code/dbxquality.png)


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