qdrant-search-quality-diagnosis

# How to Diagnose Bad Search Quality Before tuning, establish baselines. Use exact KNN as ground truth, compare against approximate HNSW. Target >95% recall@K for production. ## Don't Know What's Wrong Yet Use when: results are irrelevant or missing expected matches and you need to isolate the cause. - For a no-code quick check, use the Web UI's ANN Recall tab to compare approximate vs exact `recall@k` [Web UI ANN Recall](https://skills.qdrant.tech/md/documentation/tutorials-search-engineering/ann-recall/?s=measure-ann-recall-with-the-web-ui) - For the same comparison in code (CI gating, regression tests), run each query twice — once approximate, once with `exact=true` — and compute `recall@k` from the overlap [ANN recall in CI](https://skills.qdrant.tech/md/documentation/tutorials-search-engineering/ann-recall/?s=automate-in-ci-with-python) - Exact search bad = model or search pipeline problem. Exact good, approximate bad = tune HNSW. - Check if quantization degrades quality (compare with and without) - Check if filters are too restrictive (then you might need to use ACORN) - If duplicate results from chunked documents, use Grouping API to deduplicate [Grouping](https://skills.qdrant.tech/md/documentation/search/search/?s=grouping-api) Payload filtering and sparse vector search are different things. Metadata (dates, categories, tags) goes in payload for filtering. Text content goes in sparse vectors for search. ## Approximate Search Worse Than Exact Use when: exact se...