Book: RAG Pocket Guide: Retrieval, Chunking, and Reranking Patterns for Production
Also by me: Thinking in Go (2-book series) — Complete Guide to Go Programming + Hexagonal Architecture in Go
My project: Hermes IDE | GitHub — an IDE for developers who ship with Claude Code and other AI coding tools
Me: xgabriel.com | GitHub
You retrieve the top 10 chunks, paste them into the prompt, and send it to the model. Each chunk is 400 tokens. That is 4,000 tokens of context for a question whose answer lives in two sentences buried in chunk 6. You pay for all 4,000 on input. You also pay a quieter tax: the model has to find the answer inside a wall of near-miss text, and longer contexts degrade answer quality even when the right fact is present.
