Once you get an answer, it is easy enough to verify it.
As other posters said, relying on LLMs for factual answers to challenging questions is error prone. I just want the LLM to give me the links and I'll then assess veracity like a normal web search. I think a web search interface allowed disambiguating multi-meaning keywords might be even better.
You’ll still want to fact check it, and there’s no guarantee it’s comprehensive, but I can’t think of another tool that provides anything close without hours of research.
I will say LLMs are great for taking an ambiguous query and figuring out how to word it so you can fact check with secondary sources. Also tip-of-my-tongue style queries.
In 2025 Google is trying very hard to serve the most profitable results instead, so it'll latch onto a random keyword, completely disregard the rest, and serve me whatever ad-infested garbage it thinks is close enough to look relevant for the query.
It isn't exactly hard to beat that - just bring back the 2010 Google algorithm. It's only a matter of time before LLMs will go down the same deliberate enshittification path.
This works nicely when the LLM has a large knowledgebase to draw upon (formal terms for what you're trying to find, which you might not know) or the ability to generate good search queries and summarize results quickly - with an actual search engine in the loop.
Most large LLM providers have this, even something like OpenWebUI can have search engines integrated (though I will admit that smaller models kinda struggle, couldn't get much useful stuff out of DuckDuckGo backed searches, nor Brave AI searches, might have been an obscure topic).