Semantic Search
Searching by meaning rather than by words — which finds what keyword search misses, and misses what keyword search finds.
When not to use it
- When users search for exact strings. Identifiers, codes, names. Semantic search will find things about them and not them.
- When your corpus is small. Under a few thousand documents, the whole apparatus may be more machinery than the problem needs.
- Alone, on any real corpus. Nearly every production system that starts semantic-only ends up hybrid.
- When your domain vocabulary is unusual and you're using an off-the-shelf embedding model. It doesn't know your words, and "similar meaning" quietly degrades.
Reach for something else instead
- BM25 / keyword search — decades old, still wins on exact terms and rare words, costs nothing.
- Hybrid with RRF — what you almost certainly want. Both retrievers, ranks fused.
- Metadata filtering — often the actual need. "Similar, from this customer, last 90 days" is a filter problem, not a search problem.
- Fine-tuned embeddings — when domain vocabulary is the bottleneck and you have labelled pairs.
Sources & further reading
- Karpukhin et al. (2020), Dense Passage Retrieval for Open-Domain Question Answering — the paper that made dense retrieval standard.
- Robertson & Zaragoza (2009), The Probabilistic Relevance Framework: BM25 and Beyond — the keyword baseline that keeps refusing to lose.
- Cormack et al. (2009), Reciprocal Rank Fusion Outperforms Condorcet and Individual Rank Learning Methods — the combiner behind hybrid search.
Primary sources, listed so you can check the claims on this page rather than take them on trust.
Where people go wrong
- Replacing keyword search rather than adding to it, then rediscovering exact-match queries the hard way.
- Using a general embedding model on a specialist corpus and blaming the retrieval system for the results.
- Ignoring the asymmetry between short queries and long passages.
- Assuming high similarity means the passage answers the question. It means the passage resembles the question.
- Combining BM25 and cosine scores by adding them. They aren't on the same scale — fuse ranks, not scores.
At a glance
FieldTools & Ecosystem
Mechanismnearest neighbours in embedding space
Beats keyword onparaphrase, synonyms, natural questions
Loses to keyword onexact strings, codes, rare terms
Best practicehybrid, fused by rank
DifficultyIntermediate
Flashcards for this concept
Question
—
Answer
—
1 / 4
Often compared with
Semantic vs. keyword search — meaning vs. strings. They fail on different queries, which is exactly why hybrid beats both.