Introduction: End-to-end generative AI agent tutorial

This generative AI agent tutorial (formerly called the AI cookbook) and its sample code take you from a proof-of-concept (POC) to a high-quality production-ready application using Mosaic AI Agent Evaluation and Mosaic AI Agent Framework on the Databricks platform. You can also use the GitHub repository as a template with which to create your own AI applications.

See a list of the pages in the Generative AI agent tutorial.

Tip

There are a few ways you can build a rag app using this tutorial:

• You only have a few minutes and want to see a demo of Mosaic AI Agent Framework & Agent Evaluation.

• You want to get directly into code and deploy a RAG POC using your data.

• You don’t have any data, but want to deploy a sample RAG application.

What do we mean by high-quality AI?

The Databricks generative AI agent tutorial is a how-to guide for building high-quality generative AI applications. High-quality applications are:

• Accurate: They provide correct responses

• Safe: They do not deliver harmful or insecure responses

• Governed: They respect data permissions & access controls and track lineage

This tutorial lays out best-practice development workflow from Databricks for building high-quality RAG apps: evaluation-driven development. It outlines the most relevant ways to increase RAG application quality and provides a comprehensive repository of sample code implementing those techniques.

The Databricks approach to quality

Databricks takes the following approach to AI quality:

• Fast, code-first developer loop to rapidly iterate on quality.

• Make it easy to collect human feedback.

• Provide a framework for rapid and reliable measurement of app quality.

This tutorial is intended for use with the Databricks platform. Specifically:

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

• Mosaic AI Agent Evaluation that 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.

Code-based workflows

Choose the workflow below that most meets your needs:

Time required	What you’ll build	Link
10 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 and deploy a POC
1 hour	Comprehensive quality, cost, and latency evaluation of your POC app	Evaluate your POC Identify the root causes of quality issues

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