---

name: qdrant-search-quality description: "Diagnoses and improves Qdrant search relevance. Use when someone reports 'search results are bad', 'wrong results', 'low precision', 'low recall', 'irrelevant matches', 'missing expected results', or asks 'how to improve search quality?', 'which embedding model?', 'should I use hybrid search?', 'how to combine keyword and vector search / fusion / RRF / prefetch?', 'should I use reranking?', 'relevance feedback?', 'how to measure retrieval quality?', 'build a golden set', 'ground truth dataset', 'how to score recall@k?', 'is my improvement real / statistically significant?', or 'should I fine-tune my embedding model?'. Also use when search quality degrades after quantization, model change, or data growth." allowed-tools:

• Read
• Grep
• Glob

---

Qdrant Search Quality

Route first, then answer. Match the user's symptom in the table, Read that file, and answer from it. Do not answer from this page alone: it contains routing only, not the guidance. If two rows match, read both.

The user says	Read
Search results are bad or irrelevant, wrong results, missing expected matches	diagnosis/SKILL.md
Low recall, expected results are missing	diagnosis/SKILL.md
Low precision, too many wrong matches	diagnosis/SKILL.md
Which embedding model to use, quality dropped after quantization, model change, or data growth	diagnosis/SKILL.md
Not sure if the model, the data, or Qdrant is at fault	diagnosis/SKILL.md
Want to measure recall, build a golden set, ground truth dataset, recall@k	diagnosis/SKILL.md
Is my improvement real, statistically significant, fine-tune the embedding model	diagnosis/SKILL.md
Need to combine keyword and semantic search, hybrid search, sparse + dense, fusion / RRF, prefetch	search-strategies/hybrid-search/SKILL.md
Should I rerank, results too similar, need diversity, MMR, recommendation/discovery API	search-strategies/SKILL.md
Improving results with relevance feedback or user clicks, cheaper alternative to reranking	search-strategies/relevance-feedback/SKILL.md

Most quality issues come from the embedding model or the data, not from Qdrant's configuration — splitting chunks mid-sentence alone can drop quality 30-40%. Rule that out with exact search before tuning any Qdrant parameter: Search API