CORE GUIDE
Metadata retrieval
Metadata retrieval uses structured fields such as tenant, document type, date, authority or product to constrain and rank evidence before semantic similarity is allowed to dominate the result set.
Mental model
Treat metadata as an explicit retrieval boundary. Semantic similarity answers what looks related; metadata answers which universe of records is eligible, current or authoritative for this query.
Why it matters
Vector search can return semantically close evidence from the wrong customer, jurisdiction, version or time period. Metadata filters and boosts make those boundaries inspectable and often cheaper than asking a model or reranker to repair an invalid candidate set after retrieval.
01
Filter eligibility before optimizing relevance
Identify fields that define hard scope—tenant, permission, locale, lifecycle state, effective date—and apply them before or during candidate retrieval. Use softer metadata such as recency or document class as ranking features when appropriate. Test filter recall so strict predicates do not hide valid evidence, and log the applied metadata constraints for diagnosis.
02
Example: the right policy text comes from the wrong region
A semantic retriever answers a UK refund question with a highly similar US policy because the wording is almost identical. Adding region and effective-date metadata constrains candidates to the applicable policy set, after which semantic ranking and reranking can focus on relevance within the valid universe.
Common failure modes
- Using semantic similarity to compensate for missing tenant or permission boundaries.
- Applying strict metadata filters without measuring whether records are consistently tagged.
- Treating metadata as display decoration instead of retrieval logic.
Engineering heuristics
- Use metadata for eligibility when a field represents a hard business boundary.
- Measure tagging quality and filter recall before depending on strict predicates.
- Trace filters and boosts alongside retrieved evidence so misses are explainable.
Takeaways
- 01Metadata can define which evidence is allowed to compete for relevance.
- 02Hard scope should be enforced before semantic ranking when possible.
- 03Metadata quality becomes retrieval quality once filters depend on it.
Reading evidence
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Used in
This Concept is reused across these canonical learning paths.