Semantic Search

Semantic search retrieves information by understanding the meaning and intent behind a query rather than matching exact keywords. It interprets concepts, context, relationships between entities, and synonyms so results align with what the user actually means. All LLM's use this to som extent

Key elements:

1. Contextual understanding - Models infer the sense of words from surrounding text. For example, “java” could mean coffee or a programming language depending on context.
   
2. Synonym and concept matching - Semantic systems treat related phrases like “car” and “automobile” as connected concepts, improving recall beyond literal matches.
   
3. Embeddings - Queries and documents are often represented as vectors in a high-dimensional space. Similar meanings produce nearby vectors, enabling similarity-based retrieval.
   
4. Intent recognition - The system leverages linguistic cues to determine what the user is trying to accomplish, not just what words they typed.
   
5. Entity and relationship awareness - Knowledge graphs or trained models can recognize that “Einstein” is a person, a physicist, and related to “relativity.”
   
   

Semantic search yields more accurate and useful results than keyword search because it aligns results with meaning rather than text overlap. This also means that techniques like search word repetition is slowly fading in performence. Thank god for that.