AI & Development
AI semantic search and traditional keyword search solve different problems. Learn when each is appropriate, how hybrid search combines them, and the real
Search has been a solved problem in software for decades - Elasticsearch, Solr, and their cloud variants power search across the web's major applications. The introduction of semantic search, based on embedding models and vector similarity, does not replace this infrastructure but adds a new capability: finding results that match the meaning of a query rather than the keywords. Understanding when this capability produces better results, and when it does not, determines whether migrating to or supplementing with AI search is the right investment for your application.
Traditional keyword search (BM25 and its variants, as implemented in Elasticsearch and similar systems) is highly effective for queries where the user knows the specific term they are looking for. "Error code 404" will find documents that contain "404" reliably. A product code, a person's name, a specific technical term - keyword search is exact and predictable for these cases. It is also fast (sub-millisecond for typical indexes), cost-effective (no embedding model or vector index required), and interpretable (you can explain why a result was returned).
Keyword search fails when the user's query vocabulary diverges from the document vocabulary. A user searching for "leg pain when walking" may want documents about "intermittent claudication" - a medical term for exactly that symptom. Keyword search will not surface those documents unless the user uses the medical terminology. Semantic search handles this vocabulary mismatch problem.
Semantic search, powered by embedding models and vector similarity, finds documents by conceptual similarity rather than keyword overlap. The query "how to make pasta faster" will surface documents about reducing cooking time for pasta even if they use phrases like "speed up pasta preparation" or "quick pasta techniques" rather than the exact query words. This semantic flexibility dramatically improves recall for exploratory queries, natural language questions, and cross-lingual search.
Semantic search also handles query intent better for conversational or question-based queries. "What should I do if my app is being slow?" is a natural language question that semantic search handles well; keyword search would require the user to know the relevant technical terms ("performance optimization", "latency debugging") to find the right results.
Production search systems that have adopted AI search have largely converged on hybrid search: running both keyword and semantic search, then combining the results using reciprocal rank fusion or a re-ranking model. Hybrid search consistently outperforms either approach alone because each approach has complementary strengths.
Keyword search catches exact term matches that semantic search misses (product codes, named entities, technical identifiers). Semantic search catches conceptually related results that keyword search misses (vocabulary mismatches, natural language queries). The combination covers both failure modes. Reciprocal rank fusion - a simple algorithm that combines ranked lists from multiple retrievers - provides a solid baseline for fusion. Cross-encoder re-ranking - using a small model to score each candidate against the query - provides better precision at higher latency cost.
Adding semantic search is worth the investment when: your users search in natural language rather than keywords, your content uses technical vocabulary that your users may not know, you observe high zero-results rates on keyword search despite having relevant content, or you are building conversational search where the query is a sentence or paragraph rather than a keyword phrase.
Semantic search is not worth adding when: your queries are primarily exact lookups (IDs, names, codes), your search volume is low (the infrastructure overhead is not justified), your users are technical and comfortable with keyword query syntax, or your content is so specialized that general-purpose embedding models do not understand the domain vocabulary (in which case domain-specific embeddings or fine-tuned models are needed before semantic search will work correctly).