Skip to content

Latest commit

 

History

History
16 lines (12 loc) · 1.52 KB

README.md

File metadata and controls

16 lines (12 loc) · 1.52 KB

Retrieval Augmented Generation

Please visit http://ai-cookbook.io for the accompanying documentation for this repo.

This repo provides learning materials and production-ready code to build a high-quality RAG application using Databricks. The Mosaic Generative AI Cookbook provides:

  • A conceptual overview and deep dive into various Generative AI design patterns, such as Prompt Engineering, Agents, RAG, and Fine Tuning
  • An overview of Evaluation-Driven development
  • The theory of every parameter/knob that impacts quality
  • How to root cause quality issues and detemermine which knobs are relevant to experiment with for your use case
  • Best practices for how to experiment with each knob

The provided code is intended for use with the Databricks platform. Specifically:

  • Mosaic AI Agent Framework which provides a fast developer workflow with enterprise-ready LLMops & governance
  • Mosaic AI Agent Evaluation 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

Alt text