Semantic Search vs Keyword Search: Architecture and Implementation Guide (2026)

Q: What is the core difference between semantic search and keyword search?

Keyword search matches documents that contain the same words as the query — it operates on vocabulary. Semantic search matches documents that express the same meaning as the query — it operates on learned vector representations of concepts. A keyword search for "employee onboarding" will not find a document titled "new hire orientation procedure" unless those exact words appear. A semantic search will find it because the embedding vectors for both phrases occupy similar positions in the semantic space.

Q: What is an embedding in semantic search?

An embedding is a dense numerical vector — typically 768 to 3072 floating-point numbers — produced by a neural language model from a piece of text. The embedding model is trained so that texts with similar meanings produce vectors that are close together in the high-dimensional vector space (measured by cosine similarity or dot product). Semantic search finds documents whose embeddings are closest to the query embedding, regardless of the specific words used.

Q: When should you use keyword search instead of semantic search?

Keyword search is the right choice when users search for exact strings — product codes, error codes, legal citations, reference numbers — or when the corpus is small (under ten thousand documents) and the operational overhead of a vector store and embedding pipeline is not justified. BM25 remains a strong baseline for well-structured documents with consistent, precise vocabulary. Adding semantic search only makes sense when vocabulary mismatch is a measurable failure mode, not when it is merely a theoretical concern.

Q: What is hybrid search and why is it recommended for production?

Hybrid search combines keyword retrieval (BM25 or sparse vectors) and semantic retrieval (dense vector ANN search) and merges their result lists using reciprocal rank fusion or a learned fusion model. It is recommended for production because the two approaches have complementary failure modes: keyword retrieval fails on vocabulary mismatch but succeeds on exact matches; semantic retrieval handles vocabulary mismatch but can surface topically related but not specifically relevant documents. Combining them typically outperforms either approach alone by 10-20% on recall and precision metrics.

Q: What is reranking and does every semantic search system need it?

Reranking is a second-pass relevance scoring step applied to the top candidates from initial retrieval. A cross-encoder reranker takes each (query, document) pair as joint input and produces a more accurate relevance score than the bi-encoder similarity used in initial retrieval. Reranking typically improves precision by 15-25%. It is not required for every system — it adds 20-100ms of latency depending on implementation — but it is strongly recommended for any application where retrieval precision directly affects user experience or downstream model output quality, such as RAG pipelines.

Q: What chunking strategy should I use for semantic search?

For most document types, start with paragraph or section-boundary chunking targeting 300-500 tokens per chunk, with 10-15% overlap between adjacent chunks to avoid cutting important context at boundaries. Store parent document metadata (title, section, source) alongside each chunk so results can be contextualised. Avoid embedding entire long documents as single vectors — the meaning gets averaged across all topics and retrieval quality degrades. Fine-grained chunking strategies are worth exploring after you have measured that chunking quality is the limiting factor on your evaluation metrics.

Q: How do I measure whether my semantic search system is performing well?

Build a golden evaluation dataset of 100-500 representative queries with manually labelled relevant documents. Measure Recall@K (fraction of relevant documents in the top-K results — the most important metric for RAG), Precision@K (fraction of top-K results that are actually relevant), and MRR (mean position of the first relevant result). Re-evaluate against this dataset before every significant architectural change. A system whose retrieval quality has not been measured has not been validated — anecdotal inspection of ten queries is not a substitute for systematic evaluation.

Q: Should I use a managed vector store or self-host one?

Use a managed vector store (Pinecone, Weaviate Cloud, Qdrant Cloud) when reducing operational overhead is a priority and data residency constraints permit it. Self-host (Qdrant OSS, Weaviate OSS, pgvector) when you need full control over data placement, when data sovereignty requirements prohibit sending document content to external services, or when query volume makes managed pricing uneconomical. pgvector is a pragmatic starting point for teams already running PostgreSQL — it enables hybrid SQL and vector queries in a single database without adding a new infrastructure component.