LLMs are used in search engines to improve query understanding, rank results, and provide more relevant answers. They analyze search queries to identify the user’s intent, even when the query is vague or complex. For example, if a user searches for “best programming language for beginners,” an LLM helps the engine understand the context and return appropriate results.
LLMs are also used to generate direct answers for certain queries, bypassing the need for users to sift through multiple links. These are often presented as featured snippets or conversational responses. Additionally, LLMs assist in improving the ranking of search results by analyzing the relevance of webpages to the query, considering factors like content quality and context.
Developers leverage LLMs in search engines to enhance features like autocomplete or semantic search. Semantic search allows the engine to retrieve results based on meaning rather than exact keyword matches. For instance, searching for “how to cook pasta” could return results that include “pasta cooking tips.” This makes LLMs integral to providing smarter and more user-friendly search experiences.