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qdrant-landing/content/course/essentials/day-3/_index.md

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 title: "Day 3: Hybrid Search"
 short_description: "Day 3 of Qdrant Essentials: sparse vectors, inverted indexes, BM25, hybrid search, and score fusion with the Universal Query API."
-description: Learn how to combine dense and sparse vector search in Qdrant. Master hybrid search, score fusion, and keyword indexing to boost retrieval precision and recall. 
+description: "Learn how to combine dense and sparse vector search in Qdrant. Master hybrid search, score fusion, and keyword indexing to boost retrieval precision and recall."
 isLesson: true
 weight: 40
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qdrant-landing/content/course/essentials/day-3/hybrid-search-demo.md

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 title: "Demo: Implementing a Hybrid Search System"
 short_description: "Implement hybrid search end to end: dense plus sparse named vectors, RRF fusion, and side-by-side comparisons of retrieval strategies."
-description: Step-by-step demo on implementing hybrid search using Qdrant’s Universal Query API. Explore dense vs. sparse search, score fusion algorithms, and real-world evaluation techniques.
+description: "Step-by-step demo on implementing hybrid search using Qdrant’s Universal Query API. Explore dense vs. sparse search, score fusion algorithms, and real-world evaluation techniques."
 weight: 5
 isLesson: true
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qdrant-landing/content/course/essentials/day-3/hybrid-search.md

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 title: "Hybrid Search and the Universal Query API"
 short_description: "Combine dense and sparse vectors with Qdrant's Universal Query API and Reciprocal Rank Fusion to serve both semantic and keyword queries."
-description: Master hybrid search in Qdrant using dense and sparse vectors. Explore retrieval, reranking, and Reciprocal Rank Fusion (RRF) to build efficient, adaptive search experiences.
+description: "Master hybrid search in Qdrant using dense and sparse vectors. Explore retrieval, reranking, and Reciprocal Rank Fusion (RRF) to build efficient, adaptive search experiences."
 weight: 4
 isLesson: true
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qdrant-landing/content/course/essentials/day-3/pitstop-project.md

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 title: "Project: Building a Hybrid Search Engine"
 short_description: "Build a hybrid search engine with dense and sparse vectors, fuse results with RRF, and benchmark against single-vector baselines."
-description: Build a hybrid search engine in Qdrant combining dense and sparse vectors with Reciprocal Rank Fusion. Compare performance, optimize retrieval, and understand when hybrid search outperforms single-vector methods.
+description: "Build a hybrid search engine in Qdrant combining dense and sparse vectors with Reciprocal Rank Fusion. Compare performance, optimize retrieval, and understand when hybrid search outperforms single-vector methods."
 weight: 6
 isLesson: true
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qdrant-landing/content/course/essentials/day-3/sparse-retrieval-demo.md

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 title: "Demo: Keyword Search with Sparse Vectors"
 short_description: "Run keyword retrieval with sparse vectors in Qdrant using BM25 and SPLADE-style neural sparse models for precise lexical matching."
-description: Hands-on sparse retrieval in Qdrant—create BM25 collections, enable IDF, index with FastEmbed, try SPLADE++ expansion, and execute keyword queries via the Universal Query API.
+description: "Hands-on sparse retrieval in Qdrant—create BM25 collections, enable IDF, index with FastEmbed, try SPLADE++ expansion, and execute keyword queries via the Universal Query API."
 weight: 3
 isLesson: true
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qdrant-landing/content/course/essentials/day-3/sparse-vectors.md

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 title: "Sparse Vectors and Inverted Indexes"
 short_description: "Use sparse vectors and inverted indexes for keyword search and recommendations, with index-value pairs and dot-product scoring."
-description: Learn sparse vectors and inverted indexes in Qdrant, create named sparse vectors, store index–value pairs, run exact dot-product search, and prepare for hybrid search with dense vectors.
+description: "Learn sparse vectors and inverted indexes in Qdrant, create named sparse vectors, store index–value pairs, run exact dot-product search, and prepare for hybrid search with dense vectors."
 weight: 2
 isLesson: true
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