Pure vector search captures meaning but misses exact matches. On enterprise data that's a frequent disappointment.
01. Where It Breaks
When someone searches a stock code, policy number or contract reference, semantic search doesn't help. The model sees that code as close to a "similar" code and returns the wrong document.
02. The Hybrid Setup
The fix is running both searches: semantic search catches the concept, keyword search catches the exact match. The two result lists are merged into a single ranking.
03. Merging Results
The common method is a scoring scheme that blends the ranks from both lists. Adding raw similarity scores directly gives wrong results because the scales differ.
04. Re-Ranking
Re-ranking the top twenty of the merged list with a stronger model lifts precision noticeably. The cost is low because it applies only to candidates, not the whole archive.
05. Filter First
Apply date, department or document-type filters before the search. Shrinking the irrelevant candidate pool both speeds things up and improves precision.
06. Measurement
Measure search quality separately from the answer. Does the right document appear in the top five? If you can't answer that, you can't tell whether a bad answer came from retrieval or the model.