What is the difference between a search engine and a large language model?

What is the difference between a search engine and a large language model?

A search engine primarily helps locate information that already exists in an indexed collection. A large language model generates output from patterns learned during training and the context provided at the time of use.

These roles are different, although modern search and assistant products can combine them.

Retrieval finds relevant material

A search system receives a query, identifies potentially relevant records, and ranks results. For web search, those records often represent pages discovered and processed earlier.

If you search for a software library's release notes, the useful result is a link to an existing document. You can inspect the original wording, date, and publisher.

Generation creates a response

An LLM can turn a prompt into a new explanation, summary, or draft. It can adjust wording for an audience and combine information into a requested format.

For example, after you provide release notes, a model can summarize the changes for a beginner. That is a different operation from finding the release notes in the first place.

A generated answer can be useful even when no single source contains the exact response. That flexibility also makes it possible to generate unsupported details.

Products can use both

An assistant may search first and then generate an explanation grounded in the retrieved pages. OpenAI's web-search documentation describes allowing models to access current web information and include citations.

Choose the workflow according to the task. Finding a current specification calls for source retrieval; rewriting a paragraph may need only the paragraph and clear instructions.

When both are involved, check two things separately: whether the retrieved sources are suitable and whether the generated answer accurately represents them.