Engineer releases rag-index-validation scripts for RAG vector checks

An engineer presents a four-layer validation framework for checking a retrieval-augmented generation (RAG) vector database before querying a language model. The method uses cheap deterministic checks on documents and chunks, verifies vector embeddings and approximate search recall against exact searches, tests retrieval against a golden set, and applies calibrated judges only to the final answers. Testing on a specification corpus revealed structural failures, markup issues, and edge cases that generation metrics alone missed. Open article →