Individual skill
qdrant-search-quality-diagnosis
Diagnoses Qdrant search quality issues. Use when someone reports 'results are bad', 'wrong results', 'not relevant results', 'missing matches', 'recall is low', 'approximate search worse than exact', 'which embedding model', or 'quality dropped after quantization'. Also use when search quality degrades without obvious changes.
skills/qdrant-search-quality/diagnosis/SKILL.md
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- Last checked
- Nov 4, 2026
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- Repository last updated
- Nov 6, 2026
- License
- MIT
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- ai Repository topic
- github-copilot Repository topic
- prompt-engineering Repository topic
- hacktoberfest Repository topic
- agent-skills Repository topic
- agents Repository topic
- awesome Repository topic
- custom-agents Repository topic
- engineering Derived category
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SKILL.mdcore · 3,710 bytes
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# 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.
- Test with `exact=true` to bypass HNSW approximation [Search API](https://search.qdrant.tech/md/documentation/tutorials-search-engineering/retrieval-quality/?s=standard-mode-vs-exact-search)
- 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://search.qdrant.tech/md/documentation/search/search/?s=grouping-api)
