RAG Without the Hype: Make Retrieval Observable, Testable, and Replaceable

How my agent actually finds answers — and what happens when it doesn't

Part 5 findings of an experiment: building an LLM-powered support agent with deterministic boundaries. The companion repo contains the full code.

"What's your refund policy?"

Something has to know the answer. The model doesn't. Not reliably.

The answer lives in documents the company wrote. Getting the right one in front of the model at the right moment has an intimidating name: retrieval-augmented generation (RAG). And most explanations make it sound like magic.

RAG Without the Hype: Make Retrieval Observable, Testable, and Replaceable

Related reading

Your RAG Agent Is Retrieving the Wrong Chunk: 5 Failure Modes We Fix in…

RAG Solved the Wrong Problem: What Actually Makes AI Applications Reliable?

Retrieval Is Not Memory

Why RAG gives wrong answers (and how to fix retrieval failures)

Your RAG Isn't Broken. Your Retrieval Pipeline Is.

RAG Explained: Retrieve, Then Answer (the Prompt That Kills Hallucinations)