RAG Deep Dive

RAG Deep Dive

Duration: 2 Days

Description

Retrieval-augmented generation (RAG) grounds language models in your own data, and doing it well is one of the most valuable skills in applied AI. This in-depth course goes well beyond a basic RAG pipeline to cover the techniques that make retrieval accurate and reliable: chunking and embedding strategies, advanced retrieval and reranking, evaluation, and handling the failure modes that plague naive implementations. Through hands-on exercises, participants build and systematically improve a RAG system and learn to diagnose why retrieval succeeds or fails.
Class time is divided between instructor-led explanation (35 percent) and hands-on development (65 percent).

Audience

Developers and data/ML practitioners building systems that ground LLMs in custom data. Best suited to those with prior AI development or ML experience.

Objectives

  • Explain how retrieval-augmented generation works and why it matters
  • Design effective chunking and embedding strategies
  • Implement and tune retrieval and reranking
  • Evaluate RAG quality systematically
  • Diagnose and fix common RAG failure modes
  • Build an accurate, reliable RAG system

Prerequisites

Participants should have prior AI development or ML experience. The following will help:

  • Working knowledge of Python (see Python Foundations)
  • Experience building LLM applications (see Building LLM Applications) or ML foundations
  • Familiarity with embeddings and vector search is helpful

Course Outline

  • Module 1 – Course Introduction
    • Welcome and objectives
    • Why RAG: grounding and its limits
    • Anatomy of a RAG system
  • Module 2 – Preparing Knowledge
    • Document processing and chunking strategies
    • Embeddings: choosing and using models
    • Metadata and structure
    • Hands-on exercise: build a knowledge base
  • Module 3 – Retrieval
    • Vector search fundamentals
    • Hybrid and keyword retrieval
    • Reranking and filtering
    • Hands-on exercise: improve retrieval quality
  • Module 4 – Generation and Grounding
    • Prompting with retrieved context
    • Citations and faithfulness
    • Handling missing or conflicting information
    • Reducing hallucination
  • Module 5 – Evaluation and Failure Modes
    • Measuring retrieval and answer quality
    • Building evaluation sets
    • Common failure modes and fixes
    • Hands-on exercise: evaluate and tune a RAG system
  • Module 6 – Course Wrap-Up
    • Key takeaways
    • Advanced directions
    • Resources for continued learning