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Dense retrieval can find semantically related text. Lexical or sparse retrieval can preserve exact terms, codes and identifiers. Hybrid search combines signals, while reranking reorders a shortlist using a further scoring step.
A fictional support question containing a product code is a useful test. A semantically similar document about the wrong product may look convincing while failing the task.
Establish separate baselines
Evaluate lexical retrieval, dense retrieval and a hybrid configuration on the same labelled queries. Add reranking only after you understand the first-stage results. Record both the candidate set and the final ordering.
Use cases should include exact identifiers, paraphrases, ambiguous terms and questions with no relevant document. Track a retrieval metric such as recall at a stated cutoff, along with latency and a review of irrelevant matches.
Do not optimise one convenient example
A fusion method may improve one query type while hurting another. A reranker cannot recover a document that never entered its candidate set. Increasing the shortlist may improve coverage while increasing computation and context-management work.
Publish the corpus characteristics, relevance labels and tuning split when sharing an experiment. Keep a held-out set so repeated adjustments do not become a disguised fit to the evaluation questions.
Discussion: In your own data, do failures come mainly from exact-match misses, semantic misses or poor ordering of otherwise relevant results?
Reference: Qdrant hybrid and multi-stage queries.
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