RAG Works Better When Your Data Is Already Tidy
Retrieval gets easier when documents, embeddings, and metadata are treated like product data instead of side files.
1. Bad chunks create polite nonsense
If the source documents are messy, retrieval will still return something. It just will not return something you can trust. Clean inputs matter more than prompt tricks.
2. Metadata is not optional
Document type, source, freshness, and ownership help you filter results before the model starts talking. That is a simple way to improve answer quality.
3. Keep the retrieval path observable
Log the chunks selected, the scores returned, and the fallback behavior. Without that, every weak answer turns into a mystery hunt.
4. Start with narrow questions
RAG tends to work best when the problem is scoped and the corpus has some discipline. I trust it more for bounded knowledge than for vague everything-search.