In one sentence
Semantic search finds information by meaning rather than by matching exact words.
The simple model
Traditional keyword search looks for the words you typed. If you search "cancel subscription" it finds documents containing those words. If the right document says "end your membership", keyword search misses it.
Semantic search looks for meaning. It can find "end your membership" for a question about cancelling, because the two mean nearly the same thing. It does this using embeddings, which let software compare meaning as a calculation.
Why you care
Semantic search is what makes RAG work across real company documents, where people and documents describe the same thing in different words. Without it, an AI assistant would only find passages that happened to use the same wording as the question.
It is especially useful when your sources are large, varied, or written by different people over time.
Where it helps and where it misleads
Semantic search is powerful but not magic. Two failure modes are worth knowing:
- It can return something that sounds related but is not the answer. Closeness in meaning is not the same as correctness.
- It can miss an exact term that matters, like a specific product code, because it is focused on meaning rather than precise strings.
For this reason, many real systems combine semantic search with keyword search, so exact terms and general meaning are both covered.
A small example
An internal helpdesk let staff ask questions in their own words. Keyword search failed whenever the handbook used official terms and staff used everyday ones. Semantic search bridged that gap, while a keyword fallback made sure exact policy numbers still matched.
What this does not solve
Finding a passage that matches in meaning does not confirm it is correct, current or the right source among several. Retrieval is the first step; checking and citing the source still matter. See cite the source.
