Semantic search retrieves content by meaning instead of exact-keyword overlap. Where old keyword search rewarded pages that repeated the searched phrase, semantic search uses embeddings to match the intent behind a question to passages that genuinely answer it. Even if they use different words.
This is how AI engines find candidate sources before composing an answer. A question about "shoes that won't hurt my knees on pavement" can surface a page about cushioning and impact protection that never uses the word "knees," because the meanings align.
For brands, it ends keyword-stuffing as a strategy and rewards genuine topical depth. Cover the concept thoroughly. Its synonyms, sub-topics, and the adjacent questions buyers ask, so your content matches a wider range of fanned-out sub-queries. Writing clearly for meaning, not for one exact phrase, is the core of generative engine optimization.