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