Book: RAG Pocket Guide: Retrieval, Chunking, and Reranking Patterns for Production

Also by me: Database Playbook: Choosing the Right Store for Every System You Build

My project: Hermes IDE | GitHub — an IDE for developers who ship with Claude Code and other AI coding tools

Me: xgabriel.com | GitHub

The tutorial RAG works. You ingest a PDF, you ask it three pre-baked questions, the demo blog post writes itself. Then you put it in front of customers and watch the ground move under you.

Five failures keep showing up. They are not the ones you read about. They are the ones that survive your eval suite, slip past your CI, and only surface when an account manager forwards a screenshot of a confidently wrong answer. The RAG-as-data-engineering essay on Datalakehousehub and the field postmortem on Decompressed catalogue the same shape. This post walks through five of them, each with a code mitigation small enough to drop into a real pipeline.

1. Stale embeddings nobody re-indexed

The corpus is alive.…