If your users search for "forgot password" but your site only understands "reset credentials," you have a search problem. Standard keyword search was the gold standard for a decade, but users now expect the intelligence of Google and ChatGPT: they ask questions and use natural language.
The limitation of lexical search
Lexical search is literal: it looks for the exact string the user typed. If a user searches for "how to connect Stripe" but your documentation says "payment gateway integration," a lexical search returns zero results.
Enter semantic search
Semantic search converts your content into vectors (embeddings) that represent meaning. When a user types a query, the engine finds the documents closest in meaning to the intent.
- Understands synonyms: "laptop" and "notebook" match without manual tagging.
- Handles typos gracefully: misspellings rarely break retrieval.
- Understands context: "Apple" near "iPhone" is the company; near "pie" it is the fruit.
How to implement it (RAG)
- Vectorize your data: run help docs, catalogs, or knowledge-base articles through an embedding model (for example OpenAI's
text-embedding-3-small; see our OpenAI API guide). - Store in a vector database: pgvector, Milvus, Qdrant, or Pinecone.
- Retrieve and generate: find the nearest vectors for each query and optionally pass them to an LLM (such as Claude; see the Claude API guide) to write a direct, conversational answer.
Losing sales to a weak search bar?
Every failed search is a customer who leaves. We'll show you how much a smarter search could add to your conversions. Free, no obligation.