qdrant-search-quality

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?', 'should I use reranking?', 'how to measure retrieval quality?', 'build a golden set', 'ground truth dataset', or 'how to score recall@k?'. Also use when search quality degrades after quantization, model change, or data growth.
allowed-tools	["Read","Grep","Glob"]

Qdrant Search Quality

Symptom to Sub-skill Map

Symptom	Sub-skill
Search results are bad or irrelevant	Diagnosis and Tuning
Low recall, expected results are missing	Diagnosis and Tuning
Low precision, too many wrong matches	Diagnosis and Tuning
Quality dropped after quantization or a model change	Diagnosis and Tuning
Not sure if the model, the data, or Qdrant is at fault	Diagnosis and Tuning
Want to measure recall or build a golden set	Diagnosis and Tuning
Need to combine keyword and semantic search	Search Strategies
Want hybrid search, reranking, or score fusion	Search Strategies
Improving results with relevance feedback or recommendations	Search Strategies

First determine whether the problem is the embedding model, Qdrant configuration, or the query strategy. Most quality issues come from the model or data, not from Qdrant itself. If search quality is low, inspect how chunks are being passed to Qdrant before tuning any parameters. Splitting mid-sentence can drop quality 30-40%.

• Start by testing with exact search to isolate the problem Search API

Diagnosis and Tuning

Isolate the source of quality issues, establish labeled baselines to measure recall and relevance, tune HNSW parameters, and choose the right embedding model. Diagnosis and Tuning

Search Strategies

Hybrid search, reranking, relevance feedback, and exploration APIs for improving result quality. Search Strategies