Semantic search is a search approach that retrieves results based on the meaning of a query rather than exact keyword matches. Instead of looking for documents that contain the specific words a user typed, semantic search looks for content that is conceptually relevant, capturing intent and context rather than surface-level terms.
Capturing intent and context is made possible by embeddings, which encode meaning numerically so that similar concepts end up close together regardless of the words used to express them. A semantic search for “how do I cancel my subscription” can surface content about “ending a plan” or “account termination” without those phrases appearing in the query at all.
Semantic search is the retrieval mechanism behind many modern AI search products and is a foundational component of RAG architectures, where relevant content needs to be retrieved before a language model can generate a grounded response.
Why This Matters for Technical Writers
Semantic search changes what good documentation looks like. In a keyword-based search environment, writers often optimize for specific terms they expect users to search for. In a semantic search environment, clarity of meaning matters more than keyword density. Content that explains concepts clearly and consistently is more likely to be retrieved accurately than content that is keyword-optimized but ambiguous. Understanding semantic search helps technical writers think about documentation as something that needs to be meaningfully clear, not just correctly worded.
Common Confusion
Semantic search is sometimes treated as simply “smarter keyword search,” but the underlying mechanism is fundamentally different. Keyword search matches text patterns. Semantic search matches meaning encoded as numbers. This distinction matters when explaining to users why search results may include content that shares no words with their query, or why rephrasing a question can change results even when the intent is the same.
Related Terms
Embeddings, vector database, RAG, AI search and retrieval, large language model (LLM), knowledge base