Author(s): Words by Dharani

Originally published on Towards AI.

What retrieval failures taught me and why I built a small Python exercise that fails on purpose.

I built a RAG system with AI assistance before I properly understood how RAG worked. My approach was to build first, then “reverse engineer to understand.” I could look at the code, ask AI to explain each part, and gradually work through what it was doing. But when the output went wrong, I still needed AI to help me figure out why.

The article explains how retrieval errors are different from understanding “why the code is right,” focusing instead on diagnosing what went wrong in the pipeline—how much gets retrieved, which chunks are kept, and how they’re ordered. The author describes using thresholds and reranking to address over-retrieval and prioritization, and notes that debugging becomes clearer when retrieval decisions are treated as inspectable steps. They then propose an intentional learning exercise: a small RAG debug lab that deliberately fails by removing heading context during chunking, causing the retriever to select evidence for the wrong “workspace” even though relevant information exists. The lab uses a simplified word-overlap retriever (no full chatbot) and includes checks and a corrected solution that preserves headings to restore the intended ranking. Finally, the author discusses how AI should play a different role in learning—guiding evidence inspection and explanation-building rather than directly repairing answers—and concludes by reflecting on their goal: being able to explain how a wrong retrieved passage was produced.

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Published via Towards AI