Semantic Search

Semantic search is a search approach that interprets the meaning and intent behind a query and returns results that satisfy it, even when the results do not contain the query's exact words. A keyword engine treats 'do estheticians stand a lot at work' as a bag of terms; a semantic engine understands it as a question about the physical demands of a job. It is the opposite of literal string matching, and it now underpins both classic web search and AI answer engines.

Google's own example comes from October 25, 2019, when it began applying BERT, a transformer language model, to ranking. Google said BERT would help Search 'better understand one in 10 searches in the U.S. in English', especially longer, conversational queries where small words change the meaning. In 'brazil traveler to usa need a visa', BERT let Search grasp that the traveler was going to the U.S., not the reverse (Google). In the same post Google noted that 15% of daily queries are ones it has never seen, which is exactly where keyword matching struggles.

Semantic search combines several techniques rather than one:

• Query understanding with language models such as BERT, which read words in the context of the whole query.
• Entity recognition, linking names in queries and pages to known people, products, places and organizations in a knowledge graph.
• Dense retrieval with embeddings and vector search, which finds passages close in meaning to the query.
• Passage-level scoring, such as Google's passage ranking, live since February 2021, which lets one relevant section count even on a page about something broader.

AI search extends the same idea. Google made Gemini 3 the default model for AI Overviews globally in January 2026, and in May 2026 made Gemini 3.5 Flash the default in AI Mode. These systems do not just match a query to pages; they rewrite it into several searches, retrieve passages by meaning, and compose an answer. OpenAI and Perplexity document the same pattern for ChatGPT search and Perplexity. Perplexity also describes querying its index 'via both modalities', semantic and lexical, as hybrid search, because meaning alone misses product codes and rare names.

For content, semantic search changes what 'optimized' means. Repeating a target keyword does little once the engine understands synonyms and intent. What helps is clear coverage of the question a searcher has: say plainly what something is, who it is for, how it compares and what it costs, using the vocabulary your audience uses. Name entities precisely and consistently, so engines can connect your pages to the right product or company; entity SEO and schema markup make those connections explicit. Write sections that answer one question each, since passage-level and chunk-level retrieval judge sections, not pages.

The practical test is simple. Take the different ways real users phrase a need, from search console queries, sales calls and AI prompts, and check whether one of your pages clearly answers each phrasing. Semantic engines will find that page for wordings you never targeted; they will not invent an answer the page does not contain.