RAG evaluation beats retrieval folklore

A Hugging Face community case study benchmarked a production RAG setup and found several common assumptions did not hold for its multilingual scientific-document corpus. Why it matters: Chunking, hybrid retrieval, reranking, and vector-store choices are corpus-dependent. Small evaluation sets can prevent teams from copying expensive RAG patterns that do not improve real answers.

Try this: Build a short answer-quality test set before changing chunking or retrieval strategy, then compare the current pipeline against one proposed improvement at a time.