Recognizing and Debugging Retrieval Gaps
Explore how to reliably recognize missing-information failures in AI features and debug retrieval issues when answers are still incorrect. This lesson guides you through key checks for retrieval effectiveness and common mistakes, helping you maintain accurate and efficient AI-powered search functionality.
The previous lesson matched a missing-information failure to retrieval as its fix. This lesson treats retrieval as something we consume, not something we build, a hosted search API, a vector database, or whatever a team already has running, and spends most of its time on recognizing the failure reliably and debugging retrieval once it’s wired in and still occasionally wrong. Building the search system underneath it, chunking strategy, embedding choice, index tuning, is a deep, separate skill this course doesn’t cover.
In this lesson, we will cover:
The signature that tells us a failure is about missing information, not something else
Wiring a retrieval step into the support-summarizer as a drop-in component
Four specific things to check when retrieval is in place and the answer is still wrong
How this shows up in real products
The three ways engineers get this wrong
The signature of a missing-information failure
A model stating a specific, wrong detail with total confidence, a policy window, a price, a deadline, is the clearest sign it never had that fact and generated something ...