Tuning Semantic Candidate Search Around Verifiable Evidence

The article argues that improving candidate search depends first on the query, the index, and a measurable definition of a good result, rather than on swapping models. It recommends labeling recruiting queries with relevant, borderline, and irrelevant judgments and keeping a holdout set to prevent overfitting. A hybrid pipeline is proposed, combining dense vector retrieval with lexical search, metadata filters for hard constraints, and a reranker. The final answer should cite the supporting candidate evidence so recruiters can verify each claim. The author also suggests failure tests, detailed logging, and weekly sampling of recruiter disagreements to guide ongoing tuning.