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 |
| 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