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qdrant-landing/content/documentation/headless/content/tutorials/search-engineering.md
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| [Relevance Feedback](/documentation/tutorials-search-engineering/using-relevance-feedback/) | Relevance Feedback Retrieval in Qdrant | <span class="pill">Python</span> | 30m | <span class="text-yellow">Intermediate</span> |
| [Collaborative Filtering](/documentation/tutorials-search-engineering/collaborative-filtering/) | Collaborative filtering using sparse embeddings. | <span class="pill">Python</span> | 45m | <span class="text-yellow">Intermediate</span> |
| [Multivector Document Retrieval](/documentation/tutorials-search-engineering/pdf-retrieval-at-scale/) | PDF RAG using ColPali and embedding pooling. | <span class="pill">Python</span> | 30m | <span class="text-yellow">Intermediate</span> |
| [Retrieval Quality Fundamentals](/documentation/tutorials-search-engineering/retrieval-quality-fundamentals/) | Understand evaluation levels and choose the right metric. | <span class="pill">Python</span> | 15m | <span class="text-yellow">Intermediate</span> |
| [Building a Golden Query Set](/documentation/tutorials-search-engineering/retrieval-quality-golden-set/) | Generate ground truth data at scale and avoid data leakage. | <span class="pill">Python</span> | 20m | <span class="text-yellow">Intermediate</span> |
| [Retrieval Quality Fundamentals](/documentation/tutorials-search-engineering/retrieval-quality-fundamentals/) | Understand evaluation levels and choose the right metric. | <span class="pill">Python</span> | 20m | <span class="text-yellow">Intermediate</span> |
| [Building a Golden Query Set](/documentation/tutorials-search-engineering/retrieval-quality-golden-set/) | Generate ground truth data at scale and avoid data leakage. | <span class="pill">Python</span> | 40m | <span class="text-yellow">Intermediate</span> |
| [Retrieval Quality Evaluation](/documentation/tutorials-search-engineering/retrieval-quality/) | Measure ANN recall and tune HNSW parameters. | <span class="pill">Python</span> | 30m | <span class="text-yellow">Intermediate</span> |
| [Hybrid Search with Reranking](/documentation/tutorials-search-engineering/reranking-hybrid-search/) | Implement late interaction and sparse reranking. | <span class="pill">Python</span> | 40m | <span class="text-yellow">Intermediate</span> |
| [Semantic Search for Code](/documentation/tutorials-search-engineering/code-search/) | Navigate codebases using vector similarity. | <span class="pill">Python</span> | 45m | <span class="text-yellow">Intermediate</span> |
qdrant-landing/content/documentation/tutorials-lp-overview.md
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| [Semantic Search Basics](/documentation/tutorials-search-engineering/neural-search/) | Deploy a search service for company descriptions. | <span class="pill">FastAPI</span> | 30m | <span class="text-green">Beginner</span> |
| [Collaborative Filtering](/documentation/tutorials-search-engineering/collaborative-filtering/) | Collaborative filtering using sparse embeddings. | <span class="pill">Python</span> | 45m | <span class="text-yellow">Intermediate</span> |
| [Multivector Document Retrieval](/documentation/tutorials-search-engineering/pdf-retrieval-at-scale/) | PDF RAG using ColPali and embedding pooling. | <span class="pill">Python</span> | 30m | <span class="text-yellow">Intermediate</span> |
| [Retrieval Quality Fundamentals](/documentation/tutorials-search-engineering/retrieval-quality-fundamentals/) | Understand evaluation levels and choose the right metric. | <span class="pill">Python</span> | 15m | <span class="text-yellow">Intermediate</span> |
| [Building a Golden Query Set](/documentation/tutorials-search-engineering/retrieval-quality-golden-set/) | Generate ground truth data at scale and avoid data leakage. | <span class="pill">Python</span> | 20m | <span class="text-yellow">Intermediate</span> |
| [Retrieval Quality Fundamentals](/documentation/tutorials-search-engineering/retrieval-quality-fundamentals/) | Understand evaluation levels and choose the right metric. | <span class="pill">Python</span> | 20m | <span class="text-yellow">Intermediate</span> |
| [Building a Golden Query Set](/documentation/tutorials-search-engineering/retrieval-quality-golden-set/) | Generate ground truth data at scale and avoid data leakage. | <span class="pill">Python</span> | 40m | <span class="text-yellow">Intermediate</span> |
| [Retrieval Quality Evaluation](/documentation/tutorials-search-engineering/retrieval-quality/) | Measure ANN recall and tune HNSW parameters. | <span class="pill">Python</span> | 30m | <span class="text-yellow">Intermediate</span> |
| [Reranking for Better Search](/documentation/search-precision/reranking-semantic-search/) | Use multivector representations for better ranking. | <span class="pill">Python</span> | 30m | <span class="text-yellow">Intermediate</span> |
| [Hybrid Search with Reranking](/documentation/tutorials-search-engineering/reranking-hybrid-search/) | Implement late interaction and sparse reranking. | <span class="pill">Python</span> | 40m | <span class="text-yellow">Intermediate</span> |
qdrant-landing/content/documentation/tutorials-search-engineering/retrieval-quality-fundamentals.md
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# Retrieval Quality Fundamentals
| Time: 15 min | Level: Intermediate | | |
| Time: 20 min | Level: Intermediate | | |
|--------------|---------------------|--|----|
Before measuring retrieval quality, it's worth understanding what you're measuring. Retrieval quality operates at three distinct levels, and it's easy to optimize for the wrong one.
qdrant-landing/content/documentation/tutorials-search-engineering/retrieval-quality-golden-set.md
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# Building a Golden Query Set
| Time: 20 min | Level: Intermediate | | |
| Time: 40 min | Level: Intermediate | | |
|--------------|---------------------|--|----|
Evaluating retrieval relevance requires a labeled dataset of queries paired with their expected relevant documents (commonly called a *golden query set* or *ground truth*). The [Retrieval Quality Evaluation](/documentation/tutorials-search-engineering/retrieval-quality/) tutorial measures **ANN recall** against exact kNN, which needs no relevance labels. This page covers the separate task of building labeled data to measure **retrieval relevance** against real user intent.