Why Your RAG System Fails (and How to Actually Fix It)

A short, practical walkthrough of what actually makes a Retrieval-Augmented
Generation (RAG) system work once it's in production, not just in a demo.
Five lessons drawn from real production experience.

What you'll learn:

• Why logging every interaction and collecting thumbs-up / thumbs-down

feedback is the single highest-leverage change you can make, before
touching retrieval at all.

• Why a bigger context window doesn't replace retrieval, and what four

things it still doesn't solve: cost and latency, access control,
freshness, and traceability.

• Why one retrieval channel is never enough, and how chunk-vector,

summary-vector, full-text, and graph search each cover a different
blind spot.

• How to read your own failures, categorize them with an LLM, and hand a

scoped, checkable fix to a coding agent like Claude Code or Codex,
rather than asking it to vaguely "make the RAG better."

• A prompt you can use to build this whole four-channel pipeline

yourself, from zero.

Timestamps:

• 0:00Course overview
• 0:36Chapter 1 · Log it, and let users rate it
• 1:48Chapter 2 · Context windows are not retrieval
• 3:11Chapter 3 · Four retrieval channels, not one
• 4:33Chapter 4 · From triage to fix
• 6:06Chapter 5 · Build your own baseline, from zero

Full written tutorial (English, French, and Chinese) and every diagram
shown in this video:
https://github.com/scheffershen/yt-rag-formation/tree/main/lessons/en/continue
https://shenyi.dev/ai-lab/rag-formation/continue/01-improve-your-rag

#RAG #RetrievalAugmentedGeneration #LLM #AIEngineering #MachineLearning