[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"glossary-semantic-search::en":3,"gloss-cluster-semantic-search::en":23,"gloss-next-semantic-search::en":9},{"slug":4,"category":5,"name":6,"definition":7,"meta_desc":8,"faq":9,"schema_markup":9,"related":10},"semantic-search","core-ai","Semantic Search","Semantic search is a search technique that retrieves results based on conceptual meaning and intent rather than requiring literal keyword overlap between the search query and the matched content — powered by embeddings, which convert both the query and every searchable document into vectors positioned by meaning in a high-dimensional space, so a search for \"affordable place to eat\" can correctly surface a document about \"budget-friendly restaurants\" even though the two phrases share zero words in common. This is a foundational shift from traditional keyword\u002Ffull-text search (which matches on exact terms, stems, and synonyms from a fixed thesaurus) to search that understands what a user actually means. For SaaS builders, semantic search is one of the most immediately valuable, relatively low-effort AI features to add to an existing product, because most SaaS products already have a searchable corpus (documentation, support tickets, internal knowledge bases, product catalogs, user-generated content) where users routinely search with natural, imprecise language that traditional keyword search handles poorly. The typical implementation: embed every document (or meaningful chunk of a document) once using an embedding model and store the vectors in a vector database; at query time, embed the user's search input the same way and run a nearest-neighbor similarity search to return the closest-matching documents, ranked by similarity score. A concrete worked example: an internal company wiki's old keyword search returns zero results for the query \"how do I get reimbursed for a work trip\" because the actual relevant document is titled \"Travel Expense Policy\" and never uses the words \"reimbursed\" or \"trip.\" Semantic search correctly surfaces that document because the embedding model recognizes the conceptual overlap between \"reimbursed for a work trip\" and \"travel expense\" even without shared vocabulary. In production, semantic search is frequently combined with keyword search in a hybrid approach (catching both exact-term matches like error codes or product SKUs, and conceptual matches), and often followed by a reranking step to further refine result ordering before presenting results to a user or feeding them into an LLM as RAG context. Semantic search isn't a strict upgrade over keyword search for every query type — queries containing exact identifiers (order numbers, product SKUs, error codes, or exact proper nouns) are often served better by literal keyword matching, since an embedding model may consider two different but superficially similar-sounding SKUs as \"close\" in vector space even though only an exact match is actually correct. This is the core justification for hybrid search architectures that combine both signals rather than replacing keyword search wholesale, giving builders the conceptual matching semantic search provides while preserving the precision keyword search offers for exact-value queries.","Semantic search finds results by meaning and intent, not exact keyword matches, using embeddings to gauge similarity between a query and documents.",null,[11,14,17,20],{"slug":12,"name":13},"embedding","Embedding",{"slug":15,"name":16},"reranking","Reranking",{"slug":18,"name":19},"retrieval","Retrieval",{"slug":21,"name":22},"vector-database","Vector Database",[24,28,32,36,39,42,45,48,51,54,57,60],{"slug":25,"category":5,"name":26,"updated_at":27},"agentic","Agentic AI","2026-08-24T02:46:36+00:00",{"slug":29,"category":5,"name":30,"updated_at":31},"alignment-tax","Alignment Tax","2026-08-24T02:46:37+00:00",{"slug":33,"category":5,"name":34,"updated_at":35},"artificial-intelligence","Artificial Intelligence (AI)","2026-08-24T02:46:38+00:00",{"slug":37,"category":5,"name":38,"updated_at":27},"attention","Attention",{"slug":40,"category":5,"name":41,"updated_at":35},"beam-search","Beam Search",{"slug":43,"category":5,"name":44,"updated_at":31},"benchmark-contamination","Benchmark Contamination",{"slug":46,"category":5,"name":47,"updated_at":31},"catastrophic-forgetting","Catastrophic Forgetting",{"slug":49,"category":5,"name":50,"updated_at":35},"computer-vision","Computer Vision",{"slug":52,"category":5,"name":53,"updated_at":31},"constitutional-ai","Constitutional AI",{"slug":55,"category":5,"name":56,"updated_at":27},"context-window","Context Window",{"slug":58,"category":5,"name":59,"updated_at":35},"deep-learning","Deep Learning",{"slug":61,"category":5,"name":62,"updated_at":27},"diffusion-model","Diffusion Model"]