Semantic Search

Semantic search is a search technique that finds results based on the meaning and intent behind a query, rather than matching exact keywords. When you search for "how to reduce employee turnover," semantic search understands you want results about staff retention, attrition, and workplace satisfaction — even if those exact words do not appear in your query.

How it differs from keyword search

Traditional keyword search works by matching the words in your query to words in documents. If you search "reduce employee turnover," it finds documents containing those specific words. This means:

• Searching "car" will not find documents that only use "automobile"
• Searching "how to make customers happy" will not find a document titled "Improving Client Satisfaction Scores"
• Slight variations in phrasing can return completely different results

Semantic search solves this by understanding what you mean, not just what you typed. It knows that "car" and "automobile" mean the same thing, and that "making customers happy" and "improving client satisfaction" are about the same concept.

How it works: embeddings explained simply

Semantic search relies on embeddings — numerical representations of text that capture meaning. Here is the simplified version:

1. Every piece of text gets converted to a list of numbers (a vector). This is done by an embedding model — a specialised AI trained to represent meaning numerically. A typical vector might be 1,536 numbers long.

1. Similar meanings produce similar numbers. The vectors for "car" and "automobile" will be very close together numerically. The vectors for "car" and "democracy" will be far apart.

1. When you search, your query is also converted to a vector. The system then finds stored vectors that are closest to your query vector — measuring distance in this numerical space.

1. Closeness in vector space equals similarity in meaning. The results returned are the documents whose meaning is most similar to your query's meaning.

You do not need to understand the mathematics behind this. The key insight is: embeddings let computers measure how similar two pieces of text are in meaning, not just in the words they contain.

Where semantic search is used

Semantic search powers several important AI applications:

• RAG systems: When an AI retrieves information from your documents to answer a question, semantic search finds the most relevant passages. This is the technology behind "chat with your documents" features.
• Internal knowledge bases: Enterprise search tools that help employees find relevant policies, procedures, and documentation, even when they do not know the exact terminology.
• E-commerce: "Cosy winter jacket" finds puffer coats, fleece-lined parkas, and wool overcoats — not just items with "cosy" in the title.
• Customer support: Matching customer questions to the most relevant help articles, even when the customer describes the problem differently than the documentation.
• Legal and compliance: Finding relevant precedents or regulations based on the nature of a case, not just keyword matching.

Tools that provide semantic search

Several platforms make semantic search accessible without deep technical expertise:

• Vector databases (Pinecone, Weaviate, Chroma) — Store embeddings and run similarity searches at scale.
• AI-enhanced search platforms (Algolia, Elastic with vector search) — Add semantic capabilities to existing search infrastructure.
• Integrated AI tools — Many AI platforms include semantic search as a feature. RAG-enabled chatbots use it under the hood.
• Embedding APIs (OpenAI, Cohere, Voyage AI) — Services that convert your text into embeddings, which you then store and search however you choose.

Semantic search is not perfect

Semantic search excels at understanding intent but can struggle with very specific queries where exact matching matters — searching for a particular error code, product SKU, or person's name. The best production systems combine semantic search with traditional keyword search (called hybrid search) to get the benefits of both.