The quick read
- Collect real questions and identify the documents that should answer them.
- Inspect whether the right document appears near the top and whether outdated or unauthorised material is excluded.
How it works
A vector search system compares a query’s embedding with stored embeddings and retrieves nearby items. The method is useful when people describe a need differently from the wording in a document. It can also return plausible but inappropriate neighbours, especially when several topics share similar language.
Build a small search test
Collect real questions and identify the documents that should answer them. Include exact product names, broad descriptions and ambiguous requests. For an illustrative internal handbook, ask both ‘How do I claim travel costs?’ and a query containing a specific expense code. The two questions may benefit from different retrieval signals.
Judge the retrieved evidence
Inspect whether the right document appears near the top and whether outdated or unauthorised material is excluded. Combine semantic matching with keyword search or metadata filters when the task requires it. A second ranking stage can help choose among candidates, but it should be evaluated rather than added automatically. The success measure is useful evidence reaching the user, not simply a technically successful database query or a large collection of stored vectors.
Sources & notes
AI-assisted editorial content checked against the linked sources.
Sources reviewed for the September 2026 launch edition.