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Definition
Semantic search is retrieval where relevance is judged by conceptual similarity between the query and candidate documents, not by literal keyword overlap.
Simple explanation
You ask 'how do I cancel my plan' and get the article titled 'Ending your subscription'. That is semantic search.
Why it matters
It is the difference between search that frustrates and search that helps. Any product where users type questions benefits.
How it works
- 1Embed query and docsTurn both into vectors.
- 2Rank by similarityReturn the closest matches.
- 3Optionally rerankUse a stronger model to reorder the top results.
Real examples
Products named for illustration only. Inclusion is not an endorsement.
- Notion, Slack, Google DriveTheir AI search features rely on semantic retrieval.
- PerplexityUses semantic retrieval over live web content.
Advantages
- Handles synonyms, paraphrases and intent.
- Better for question-style queries.
Limitations
- Can miss exact-match needs (e.g. product SKUs).
- Best combined with keyword search.
Common misunderstandings
- ClaimSemantic search understands your users.RealityIt approximates meaning; combine with feedback signals for real personalisation.
Frequently asked questions
Do I need AI to do semantic search?
You need embeddings — you don't necessarily need a full LLM in the loop.
The Tool Money Lab perspective
Semantic search is quietly one of the highest-ROI AI features. It fixes a problem users have complained about since search boxes existed.
Conclusion
Semantic search is 'what did the user mean' search. Every serious knowledge product will have it within two years.