GroveAI
technical

What is semantic search?

Quick Answer

Semantic search uses AI to understand the meaning behind search queries and documents rather than just matching keywords. It finds results based on conceptual similarity, so searching for 'annual leave policy' also returns documents about 'holiday entitlement' and 'time off procedures'. Semantic search dramatically improves search accuracy and is the retrieval mechanism behind RAG systems and modern enterprise search.

Summary

Key takeaways

  • Understands query meaning rather than just matching keywords
  • Finds relevant results even when exact terms are not used
  • Powered by embedding models that represent meaning as vectors
  • Significantly outperforms keyword search for natural language queries

How Semantic Search Works

Semantic search converts both queries and documents into vector embeddings that capture their meaning. When you search, your query is converted into a vector, and the system finds documents whose vectors are most similar, based on meaning rather than keyword overlap. This means semantic search understands synonyms (car/automobile), related concepts (budget/cost/expenditure), and context (Apple the company vs apple the fruit). It handles natural language queries that users would ask conversationally, not just keyword-style queries. The technology relies on embedding models trained on vast text corpora to understand language meaning. Modern implementations often combine semantic search with traditional keyword search in a hybrid approach, capturing both exact-match precision and semantic understanding.

Business Applications of Semantic Search

Semantic search transforms enterprise information retrieval. Internal knowledge bases become dramatically more useful when employees can search with natural questions rather than guessing the right keywords. Customer support systems can match incoming queries to relevant knowledge base articles based on meaning, improving self-service resolution rates. Legal and compliance teams can search document repositories for conceptually relevant content rather than relying on exact terminology. HR systems can match job descriptions to CVs based on semantic similarity. Contract analysis can find clauses across thousands of documents that relate to specific topics regardless of the exact wording used. In every case, semantic search reduces the time spent searching and increases the likelihood of finding the most relevant information.

Related

Related questions

what are embeddingswhat is vector databasewhat is ragbuild knowledge base for aiwhat is retrieval augmented generation

FAQ

Frequently asked questions

For natural language queries, semantic search significantly outperforms keyword search. For exact-match searches (product codes, names), keyword search remains valuable. The best systems use hybrid search combining both approaches.

Choose an embedding model, generate embeddings for your content, store them in a vector database, and build a search interface that converts queries to embeddings and finds similar content. Many platforms offer managed semantic search services.

Embedding generation costs are minimal, typically pence per thousand documents. Vector database hosting costs £50 to £500+ per month depending on scale. The main investment is in integration and tuning for your specific use case.

Modern embedding models handle common technical terminology well. For highly specialised jargon, domain-specific embedding models or fine-tuned models perform better. Hybrid search combining semantic and keyword matching ensures technical terms are matched even when the embedding model is unfamiliar with them.

Semantic search can significantly improve search quality but is best deployed alongside existing keyword search in a hybrid approach. This captures both exact-match precision for specific terms and semantic understanding for natural language queries.

Related Content

Embeddings Explained

The technology powering semantic search

Enterprise Search Guide

Implementing AI-powered search in your organisation

Knowledge Base Service

Semantic search-powered knowledge systems

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