GenAI Cookbook

TLDR; this cookbook and its sample code will take you from initial POC to high-quality production-ready application using Mosaic AI Agent Evaluation and Mosaic AI Agent Framework on the Databricks platform.

The Databricks Generative AI Cookbook is a definitive how-to guide for building high-quality generative AI applications. High-quality applications are applications that:

1. Accurate: provide correct responses

2. Safe: do not deliver harmful or insecure responses

3. Governed: respect data permissions & access controls and track lineage

Developed in partnership with Mosaic AI’s research team, this cookbook lays out Databricks best-practice development workflow for building high-quality RAG apps: evaluation driven development. It outlines the most relevant knobs & approaches that can increase RAG application quality and provides a comprehensive repository of sample code implementing those techniques.

Important

• Only have a few minutes and want to see a demo of Mosaic AI Agent Framework & Agent Evaluation? Start here.

• Want to hop into code and deploy a RAG POC using your data? Start here.

• Don’t have any data, but want to deploy a sample RAG application? Start here.

This cookbook 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

Retrieval-augmented generation (RAG)

This first release focuses on retrieval-augmented generation (RAG). Future releases will include the other popular generative AI techniques: agents & function calling, prompt engineering, fine tuning, and pre-training.

The RAG cookbook is divided into 2 sections:

1. Learn: Understand the required components of a production-ready, high-quality RAG application

2. Implement: Use our sample code to follow an evaluation-driven workflow for delivering a high-quality RAG application

Code-based quick starts

Time required	Outcome	Link
🕧 30 minutes	Sample RAG app deployed to web-based chat app that collects feedback	RAG Demo
🕧🕧🕧 2 hours	POC RAG app with your data deployed to a chat UI that can collect feedback from your business stakeholders	Build & deploy a POC
🕧🕧 1 hour	Comprehensive quality/cost/latency evaluation of your POC app	- Evaluate your POC - Identify the root causes of quality issues

Table of contents