What is semantic search in AI?
Semantic search is a search method that understands what you mean and returns results with the same meaning, even if they use completely different words.
"Semantic" means "related to meaning". A traditional search box looks for the exact words you typed. A semantic search system turns your query and every document into numbers that capture meaning, then finds the closest matches.
A simple example. You type "cheap flights to Goa". A page titled "Low-cost air tickets for your Goa trip" shares only one word with your query, yet it is exactly what you want. Semantic search can rank that page near the top because "cheap" and "low-cost", and "flights" and "air tickets", mean nearly the same thing.
Semantic search sits behind many modern AI apps, including chatbots that answer from your documents using RAG.
Semantic search vs keyword search: what is the difference?
The main difference is that keyword search matches exact words, while semantic search matches meaning.
| Feature | Keyword search | Semantic search |
|---|---|---|
| Matches on | Exact words and close spellings | Meaning and intent |
| Synonyms | Missed unless added by hand | Usually handled automatically |
| Example | "cheap flights" misses "low-cost tickets" | Finds both |
| Exact codes, names, IDs | Very good | Can be weaker |
| How it works | Inverted index, scoring like BM25 | Embeddings and vector similarity |
| Speed and cost | Fast and cheap | Needs a model to create embeddings |
| Explaining results | Easy: the word matched | Harder: closeness in a vector space |
Keyword search is still excellent when users type exact things like an order number, a product code such as "iPhone 15 Pro 256GB" or a person's name. Semantic search shines when people describe what they want in their own words, like "phone with good camera under 30k".
People also search for semantic search vs vector search. Vector search is the technique (finding nearest vectors), while semantic search is the goal (finding results by meaning). Most semantic search today is built with vector search.
How does semantic search work with embeddings?
Semantic search works by converting text into embeddings, which are lists of numbers that represent meaning, and then finding the stored embeddings closest to the query's embedding.
The pipeline has two stages:
- Indexing (done once, updated as data changes): split your documents into chunks, pass each chunk through an embedding model, and store the resulting vectors in a vector database along with the original text.
- Searching (every query): turn the user's query into a vector with the same embedding model, find the nearest stored vectors, and return the matching chunks.
Closeness is usually measured with cosine similarity, which checks how much two vectors point in the same direction:
query: "cheap flights to Goa" becomes [0.12, -0.40, 0.88, ...] document: "low-cost air tickets, Goa" becomes [0.10, -0.38, 0.85, ...] similarity close to 1.0 means similar meaning
Vector databases use approximate nearest neighbour (ANN) indexes, such as HNSW, so they can search millions of vectors quickly without comparing every single one.
Many systems add a re-ranker at the end: a second, slower model that reads the top results with the query and reorders them for better precision. My advice for beginners: get basic embedding search working on a small set of your own notes first, then add re-ranking only when you can see which results are wrong.
What is hybrid search?
Hybrid search combines keyword search and semantic search, then merges both result lists so you get exact matches and meaning-based matches together.
Why combine them? Each fills the other's gaps:
- Semantic search understands "budget phone for photography" but may miss an exact model number.
- Keyword search catches "SKU-48213" or a rare name instantly but misses synonyms.
A common way to merge results is reciprocal rank fusion (RRF), which rewards documents that rank well in either list. Search engines such as Elasticsearch and OpenSearch, and many vector databases, offer hybrid search options. For most real products, hybrid search is a sensible default rather than pure semantic search.
Semantic search in RAG, e-commerce and support: where is it used?
Semantic search is used anywhere people describe what they need in natural language and the right answer may use different words.
- RAG chatbots: before an LLM answers, semantic search pulls the most relevant chunks from your PDFs, notes or company docs. Better retrieval means fewer made-up answers.
- E-commerce: "shoes for monsoon" can surface waterproof sandals and rain boots, even if product titles never say "monsoon".
- Customer support: matching "my money got deducted but order failed" to the help article titled "Payment debited, order not placed".
- Job and education platforms: matching resumes to job descriptions, or questions to course lessons.
- Personal tools: note apps and photo apps that let you search by description.
If you are preparing for interviews, semantic search and retrieval come up often in RAG interview questions.