Search comes in two fundamentally different shapes, and the difference decides what your users can and cannot find. Keyword search looks for the words you typed. Semantic search looks for the meaning behind them, so a query for "how to reset my password" can surface a document titled "account recovery steps" even though they share almost no words. Each approach is excellent at what the other is bad at, which is exactly why the interesting answer in 2026 is rarely one or the other.
What changed in 2026
- Hybrid search became the default. Most serious search stacks now run lexical and vector search together and fuse the results, rather than choosing a side.
- Embedding costs fell. Cheaper, faster embedding models made it practical to vectorize large corpora that used to be keyword-only.
- Rerankers moved downstream of both. Teams increasingly retrieve broadly with hybrid search, then reorder with a reranker for precision, which changed how the two methods are tuned.
How each one works
Keyword search builds an inverted index mapping words to the documents that contain them, then scores matches with algorithms like BM25 that reward rare terms and penalize very long documents. It knows nothing about meaning; "car" and "automobile" are unrelated strings to it. But it is fast, transparent, and exact.
Semantic search converts text into embeddings, which are lists of numbers that place similar meanings near each other in space. A query becomes a vector, and search returns the documents whose vectors are closest. It understands paraphrase and synonymy, but it can miss an exact term and it needs a model to produce the vectors. For the underlying representation, see embeddings explained.
Side-by-side comparison
| Factor |
Keyword search |
Semantic search |
| Matches on |
Exact words |
Meaning |
| Synonyms and paraphrase |
Misses them |
Handles them |
| Exact codes, IDs, names |
Excellent |
Often worse |
| Setup cost |
Low |
Needs embeddings and a vector store |
| Explainability |
High, you see the matched terms |
Low, similarity is opaque |
| Typos |
Struggles |
More forgiving |
When to use each
Reach for keyword search when exactness matters: product SKUs, error codes, legal citations, or any query where the user knows the precise string. Reach for semantic search when users describe what they want in their own words and the vocabulary will not match your documents. In practice most applications want both, fused into hybrid search, because real queries mix precise terms with fuzzy intent. After retrieval, a second-stage reranking step squeezes out extra precision regardless of which method surfaced the candidates.
Common pitfalls
- Going all-in on vectors. Pure semantic search embarrasses itself on exact-match queries like order numbers. Keep a lexical path.
- Ignoring chunking. Semantic quality depends heavily on how you split documents. Chunks that are too big blur meaning; too small lose context.
- Not tuning the fusion. Hybrid search needs a sensible way to combine scores. Naive averaging often underperforms rank-based fusion.
- Skipping evaluation. Relevance is measurable. Without a labeled query set you are guessing whether a change helped.
FAQ
Is semantic search always better than keyword search?
No. It is better for meaning-based queries and worse for exact strings. For codes, names, and IDs, keyword search usually wins outright.
What is hybrid search?
Running keyword and semantic search together and combining their results, so you get lexical precision and semantic recall at once. It is the recommended default for most applications.
Do I need a vector database?
For semantic search at scale, yes, or a search engine with vector support. For small datasets you can compute similarity in memory, but a dedicated store pays off quickly.
How does this fit into RAG?
Retrieval is the first R in RAG, and hybrid search is a common way to do it. Many pipelines then add knowledge graphs or reranking to improve what the model finally reads.
Where to go next