Embeddings and Semantic Search Explained

Embeddings are numerical representations of meaning that let semantic search understand what a query is actually asking for, rather than only matching the literal words it contains, which is what allows modern AI search and chatbot systems to surface genuinely relevant results even when the wording differs completely.

Traditional keyword search matches exact words, missing a result that uses different phrasing for the same concept.

Semantic search converts both the query and the documents into embeddings, then finds documents whose meaning is closest to the query, even when no words overlap directly.

We use semantic search as the retrieval layer in most AI assistant projects, since it consistently surfaces more relevant results than keyword matching alone.

Under the hood, an embedding is simply a list of numbers, often hundreds or thousands of dimensions, positioned so that texts with similar meaning end up close together in that numerical space. A search for "cancel my subscription" and a document titled "how to end a recurring plan" land near each other even though they share almost no words, because the model has learned that these phrases mean roughly the same thing.

A common pitfall is assuming semantic search is a drop-in replacement for keyword search in every case. Exact matches, like a product SKU or an order number, are still handled better by traditional keyword or exact-match lookups, which is why most production systems combine both approaches rather than relying on embeddings alone.

We typically pair semantic retrieval with a lightweight keyword fallback and re-ranking step, since the combination catches both the fuzzy, meaning-based queries and the precise, exact-match ones that pure semantic search sometimes misses.