Semantic Search analyzes the underlying intent and contextual relationship of concepts within a query, moving entirely beyond traditional exact-keyword matching
Semantic search retrieves by meaning: queries and documents are embedded into vectors, similarity is computed geometrically, and top matches return even when no keywords overlap. Production stacks run hybrid retrieval, semantic plus keyword, then a reranker model orders candidates by true relevance to the query.
Semantic search is the retrieval backbone of RAG, enterprise knowledge access, and agent memory: it's why AI systems find 'credential recovery' when users ask about 'password reset'. Most enterprise AI quality complaints trace to retrieval, making semantic search tuning one of the highest-ROI skills in applied AI.
Keyword search matches literal terms; semantic search matches meaning via embeddings: synonyms, paraphrases, and related concepts connect. Keywords still win on exact identifiers and codes, which is why hybrid systems dominate.
A second-stage model that deeply scores query-document pairs from the initial candidate set, reordering for true relevance. Adding one is often the single biggest retrieval-quality upgrade available.
Usual suspects: embedding model mismatched to the domain, chunks too large or too small, missing keyword channel for exact terms, or no reranker. Diagnose with a labeled query set before swapping infrastructure.