initial commit; Claude generated SEO descriptions

qdrant-landing/content/course/multi-vector-search/module-3/_index.md

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 title: "Module 3: Scalability and Optimization"
+short_description: "Module 3: scale multi-vector search with quantization, pooling, MUVERA indexing, multi-stage retrieval, and pipeline evaluation."
 description: "Address scalability challenges in multi-vector search. Learn optimization techniques including quantization, pooling, MUVERA, and multi-stage retrieval."
 isLesson: true
 weight: 40

qdrant-landing/content/course/multi-vector-search/module-3/evaluating-pipelines.md

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 title: "Evaluating Search Pipelines"
+short_description: "Evaluate retrieval pipelines with Recall@k, NDCG, and MRR using qrels to balance cost, latency, and search quality across configurations."
 description: Learn how to evaluate different search configurations in terms of cost, latency, and retrieval quality using ground truth datasets and standardized metrics.
 weight: 5
 isLesson: true

qdrant-landing/content/course/multi-vector-search/module-3/final-project.md

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 title: "Final Project: Build Your Own Multi-Vector Search System"
+short_description: "Capstone project: build an end-to-end multi-vector search system with ColPali, optimization techniques, and a measurable quality benchmark."
 description: Apply everything you've learned to build a multi-vector search system that solves a real problem of your choosing.
 weight: 7
 isLesson: true

qdrant-landing/content/course/multi-vector-search/module-3/multi-stage-retrieval.md

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 title: "Multi-Stage Retrieval with Universal Query API"
+short_description: "Build multi-stage retrieval with the Universal Query API: prefetch with fast vectors, then rerank with ColBERT for high-quality results."
 description: Combine multiple optimization techniques in multi-stage retrieval pipelines using Qdrant's Universal Query API.
 weight: 1
 isLesson: true

qdrant-landing/content/course/multi-vector-search/module-3/muvera.md

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 title: "MUVERA"
+short_description: "Use MUVERA to approximate multi-vector documents as single vectors so HNSW can index them, enabling fast late-interaction retrieval at scale."
 description: Understand MUVERA and how it enables HNSW indexing for multi-vector search despite MaxSim asymmetry.
 weight: 4
 isLesson: true

qdrant-landing/content/course/multi-vector-search/module-3/pooling-techniques.md

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 title: "Pooling Techniques"
+short_description: "Apply pooling to multi-vector documents to cut the number of vectors per document while preserving late-interaction retrieval quality."
 description: Reduce the number of vectors per document using row/column pooling and hierarchical token pooling strategies.
 weight: 3
 isLesson: true

qdrant-landing/content/course/multi-vector-search/module-3/quantization-techniques.md

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 title: "Vector Quantization Techniques"
+short_description: "Compress multi-vector embeddings with scalar, binary, and product quantization in Qdrant to slash memory cost without losing retrieval quality."
 description: Learn how to reduce memory usage with scalar quantization, binary quantization, and other compression methods.
 weight: 2
 isLesson: true