How does metadata filtering fit into RAG retrieval, and...

Metadata filtering narrows the pool of candidate chunks using structured attributes — document type, department, version number, publication date, access level — before vector similarity search runs at all, rather than searching everything and relying on similarity scores to sort out relevance. That order matters because vector similarity has no built-in notion of “which version is current”: an outdated policy document and its replacement, both about the same topic and phrased similarly, produce nearly identical embeddings, so pure similarity search can just as easily surface the wrong one.

Filtering first — restricting the search to version='2026' AND doc_type='policy' before computing any similarity — removes the outdated document from consideration entirely instead of hoping it ranks lower than the current one. It also narrows the search space dramatically at scale: searching 500 relevant chunks instead of 500,000 is both faster and meaningfully more precise. The practical implication is that every chunk needs rich metadata attached at indexing time — a recall problem that’s easy to design away up front and expensive to fix after the fact.