Metadata Strategies
Metadata strategies for RAG: filtering, hybrid search, metadata extraction, and structured metadata for improved retrieval.
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Metadata Strategies
The hidden superpower of production retrieval. Vector similarity finds text that sounds right; metadata is what keeps you from serving the right-sounding answer from the wrong tenant, the wrong date, or a document the user isn’t allowed to see.
Semantic similarity has no idea about permissions, freshness, or provenance. A vector search will happily surface a chunk from another customer’s data or a policy that expired last year, simply because it reads as relevant. Metadata filters add the hard constraints that similarity can’t express — and in production they are often more important to get right than the embedding model itself.
Metadata filtering is often more important than vector similarity for production retrieval. A vector search might find a semantically similar chunk from the wrong department, wrong date, or wrong access level. Metadata filters prevent this.