Navigate the generative AI agent tutorial

Use this page to navigate through the generative AI agent tutorial, formerly called the AI cookbook. Follow it from end-to-end, or jump into an area that interests you.

Introduction

Introduction: End-to-end generative AI agent tutorial

10-minute demo

10-minute AI agent demo notebooks

Learn about RAG and AI agents

• Introduction to RAG
• RAG fundamentals
  • Data pipeline
  • RAG chain for inference
  • Evaluation and monitoring
  • Governance and LLMOps
• Improve RAG quality
  • Data pipeline quality
  • Rag chain quality
• Evaluate RAG application quality
  • Define quality
  • Assess performance
  • Enable quality measurement
• Evaluation-driven development

Step-by-step implementation

• Prerequisites: Gather requirements
• Step 1: Clone code and create compute
• Step 2: Deploy a Proof-of-concept to get stakeholder feedback
• Step 3: Curate an evaluation data set
• Step 4: Evaluate the Proof-of-concept quality
• Step 5: Find root causes of quality issues
  • Step 5.1: Debug retrieval quality
  • Step 5.2: Debug generation quality
• Step 6: Iteratively fix and evaluate quality
  • Step 6.1: Fix data pipeline quality
• Step 7: Deploy and monitor the AI application

Next: Introduction to the generative AI agent tutorial >