Embeddings vs Keyword Search

Search systems can match queries in two fundamentally different ways. Keyword (lexical) search matches the literal terms in a query against terms in documents. Embedding-based (semantic) search converts text into numeric vectors and matches by meaning, so it can connect queries and documents that share no words. Each excels where the other struggles.

Key Differences

Keyword search, powered by inverted indexes in engines like Elasticsearch or OpenSearch, ranks documents using algorithms such as BM25 that score term frequency and rarity. It is fast, cheap, mature, and transparent: you can see exactly which terms matched. Its weakness is vocabulary. A search for "car" will not find a document that only says "automobile," and paraphrased questions often miss.

Semantic search uses an embedding model to place text in a high-dimensional space where similar meanings sit close together. It handles synonyms, paraphrases, intent, and even cross-lingual matching gracefully, which makes it the backbone of retrieval-augmented generation (RAG). The costs are real: you must run an embedding model, maintain a vector index, and accept that results are hard to explain and that the system can miss exact identifiers, rare tokens, or precise codes that don't carry strong semantic signal.

In short, keyword search is precise about terms; semantic search is precise about meaning.

When to Choose Embeddings

Choose embedding-based search when users ask natural-language questions or when meaning matters more than exact wording. It is the right tool for conceptual matching, handling synonyms and paraphrases, supporting multilingual corpora, and feeding relevant passages to an LLM in a RAG pipeline. If your users describe what they want rather than naming it precisely, semantic search wins.

When to Choose Keyword Search

Choose keyword search when exactness is paramount: product codes, SKUs, error codes, proper nouns, and identifiers that must match precisely. It is also the better default when cost and latency are tight, when you search very large corpora cheaply, and when stakeholders need explainable, transparent ranking. Its maturity and simplicity make it hard to beat for term-centric retrieval.

Practical Considerations

Building hybrid search well takes care: you must normalize and fuse scores from two very different scoring systems, and a reranking model on the top results often delivers the largest relevance gain. Embedding quality matters enormously, so the choice of embedding model, the chunk size, and domain adaptation can outweigh the choice between methods. Keyword search remains indispensable for exact identifiers, codes, and rare tokens that semantic models handle poorly, which is exactly why hybrid retrieval is so common. Evaluate relevance with a labeled query set and metrics such as recall and mean reciprocal rank rather than impressions, since semantic results can look plausible while missing the right answer. Cost and latency budgets should account for embedding generation and the extra retrieval pass.

Verdict

These methods are complementary, not competing, and the strongest production systems use hybrid search that runs both and fuses the scores, often with a technique like reciprocal rank fusion. Hybrid retrieval catches exact-term matches and semantic matches together, then a reranker can refine the top results. Use keyword search alone for precise, term-driven lookups; use embeddings alone for conceptual queries; and reach for hybrid search when you need the strengths of both, which in practice is most of the time.