Qdrant Search Quality

From the author

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.

— the description by Qdrant

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The Qdrant Search Quality skill provides a structured diagnostic framework for identifying and resolving search relevance issues within Qdrant deployments. It helps users troubleshoot symptoms like low precision, low recall, and irrelevant results by routing them to specific guidance files based on their reported issues. The skill emphasizes that quality degradation often stems from data preparation or embedding model choices rather than database configuration, encouraging users to validate their setup through systematic testing and evaluation strategies.

What it does

Routes users to specific diagnostic documentation based on reported search symptoms.
Provides guidance on measuring retrieval quality using golden sets and recall metrics.
Offers strategies for implementing hybrid search, reranking, and relevance feedback.
Helps determine if search issues are caused by data quality, embedding models, or configuration.
Assists in validating whether search improvements are statistically significant.

When to use it

When search results are reported as irrelevant, inaccurate, or missing expected matches.
When search quality drops after changing models, applying quantization, or scaling data.
When you need to implement advanced retrieval techniques like hybrid search or reranking.
When you need to establish a baseline for retrieval quality using ground truth datasets.
When you are unsure how to optimize embedding models or combine keyword and vector search.

Advantages

Provides a clear, systematic approach to troubleshooting complex search relevance problems.
Helps isolate the root cause of search degradation between data, models, and configuration.
Facilitates the adoption of advanced search patterns like RRF and hybrid fusion.
Encourages data-driven optimization through the use of golden sets and recall metrics.

Usage examples

“My search results are irrelevant and missing expected items, how do I fix this?”: The agent will route you to the diagnosis guide to help identify if the issue lies in your data chunking, embedding model, or search configuration.
“How can I combine keyword and vector search to improve my Qdrant results?”: The agent will provide instructions on implementing hybrid search strategies, including sparse and dense vector fusion or RRF.

Install

Download the skill as a ZIP:
https://autogineer.com/agent-skills/qdrant/qdrant-search-quality/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip qdrant-search-quality.zip -d .agents/skills

Documentation by Qdrant

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

Files

ATTRIBUTION.mdAdded by AutoGineer854 B
LICENSELicence file added by AutoGineer11.1 KiB
SKILL.md2.4 KiB

SKILL.md