3 docs tagged with "rag"

How embedding models work, the 2026 model landscape, how to choose one, dimensions and Matryoshka, vector quantization, domain adaptation, hybrid retrieval, and the production pitfalls that quietly halve recall.

The design axis behind RAG, just-in-time retrieval, structured note-taking, the LLM-wiki pattern, and llms.txt -- where synthesized knowledge lives, who maintains it, and when to use each.

How retrieval-augmented generation grounds an LLM in external knowledge using embeddings and vector databases, how it compares to fine-tuning, and the production levers that make it work.