This is a solved problem. The answer is to add extra relevant information to the context as part of answering the user's prompt.

This is sometimes called RAG, for Retrieval Augmented Generation.

These days the most convincing way to do this is via tool calls.

Provide your LLM harness with a tool for running searches, and tell it to use that tool any time it needs additional information.

A good "reasoning" LLM like GPT-5 or Claude 4 can even handle contradictory pieces of information - they can run additional searches if they get back confusing results and work towards a resolution, or present "both sides" to the user if they were unable to figure it out themselves.