Phase 3: Enterprise RAG Systems · 50 min · Python · LangSmith · Pinecone

Debugging Retrieval Quality

Is the right chunk retrieved? Is it ranked high enough? Is the generation using it?

What you will learn

Apply the diagnostic flowchart: right chunk? ranked high enough? generation using it correctly?
Debug retrieval: inspect retrieved chunks, measure MRR@k, compare BM25 vs. dense results
Debug generation: check if LLM ignores context, hallucinates, or misinterprets
Debug chunking: too small (missing context) vs. too large (diluting relevance)
Diagnose production degradation: stale documents, embedding drift, query distribution shift

Objective

What you'll learn

This lesson takes approximately 50 min. By the end, you will be able to:

Apply the diagnostic flowchart: right chunk? ranked high enough? generation using it correctly?
Debug retrieval: inspect retrieved chunks, measure MRR@k, compare BM25 vs. dense results
Debug generation: check if LLM ignores context, hallucinates, or misinterprets
Debug chunking: too small (missing context) vs. too large (diluting relevance)
Diagnose production degradation: stale documents, embedding drift, query distribution shift

The Problem

The client reports the RAG system is "giving wrong answers." Where do you start? The problem could be in retrieval (wrong chunks), ranking (right chunks but wrong order), generation (right context but LLM ignores it), or the query itself. This lesson teaches the systematic debugging methodology.

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You've read the first 2 sections. The rest of this lesson covers The Concept, Build, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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