What is semantic search? Search by meaning, explained
Semantic search finds results by meaning rather than by exact keywords. It converts text into numerical vectors called embeddings and returns the items whose meaning is closest to your query, so “how do I reset my password” can match an article titled “forgot login” even though they share no words. This article contrasts keyword and semantic search, explains embeddings and vector similarity at a beginner level, and shows why semantic search sits underneath retrieval-augmented generation (RAG).
Keyword search versus semantic search
Keyword search looks for the words you typed. If a document does not contain them, or contains a synonym instead, it can be missed. Search for “cheap flights” and a page about “budget airfares” may never appear, because the vocabulary differs even though the meaning is the same.
Semantic search closes that gap by comparing meaning instead of spelling. It understands that “reset my password” and “forgot login” describe the same need, and that “budget airfares” answers the “cheap flights” question. The two are not mutually exclusive: keyword matching is still excellent for exact names, codes and quoted phrases.
How it works: embeddings and vectors
A model converts each piece of text into an embedding, a long list of numbers that captures its meaning. Texts that mean similar things end up with similar numbers, so you can think of every piece of text as a point in a space where related ideas sit close together. Our explainer on what embeddings are goes deeper on this.
At search time your query is converted into an embedding too, and the system looks for the stored items whose points are nearest to it. That nearness is called vector similarity. Nothing about it depends on shared words, which is exactly why it handles synonyms, paraphrases and loosely worded questions well.
The pieces of a semantic search system
A typical semantic search setup has a small number of moving parts:
- Prepare the content — split documents into sensible chunks so each one covers a single idea.
- Create embeddings — run each chunk through an embedding model to get its vector.
- Store the vectors — keep them in a vector database or a search service that supports similarity lookup; see our explainer on vector databases.
- Embed the query — convert the user’s question with the same model.
- Return the closest matches — rank chunks by similarity and show or use the top results.
Why it underpins RAG
Retrieval-augmented generation (RAG) gives a language model relevant information before it answers. Semantic search is the retrieval step: it finds the passages from your own documents that best match the question, and those passages are handed to the model as context. This grounds answers in real sources and reduces, though does not eliminate, made-up output.
Our explainer on retrieval-augmented generation covers the full flow. The key point here is that the quality of the answer depends heavily on the quality of the search that feeds it.
Limits and where it fits
Semantic search is not magic. It can return results that are related but not quite right, it depends on the embedding model understanding your domain, and it is weaker than keyword search for exact identifiers. Many production systems blend both approaches for that reason. Specific products and their features change often, so check the provider’s documentation for details.
Semantic search appears in foundational AI and data syllabuses as the practical use of embeddings. Our /revision library covers that material lesson by lesson, and the concepts here will carry across any cloud provider.
Original practice questions, timed mock exams and revision notes. No card, nothing to pay.
Questions, answered
Sources
Exam details in this post come from the vendor's published exam guide, which is the authority on what is tested and how.
- AWS Certified AI Practitioner (AIF-C01) exam guide — Amazon Web Services
- AWS Certified Data Engineer – Associate (DEA-C01) exam guide — Amazon Web Services