I agree with Simon’s article but I usually think about “research” to mean comparing different kinds of evidence (not just the search part). Like evidence for the effectiveness of Obamacare. Or how some legal case may play out in the courts. Or how much The Critic influenced The Family Guy. Or even what the best way to use X feature of Y library.

I’ve found ChatGPT and other LLMS can struggle to evaluate evidence - to understand the biases behind sources - ie taking data from a sketchy think tank as gospel. I also have found in my work the more reasoning, the more hallucination. Especially when gathering many statistics.

That plus the usual sycophancy can cause the model to really want to find evidence to support your position. Even if you don’t think you’re asking a leading question, it can really want to answer your question in the affirmative.

I always ask ChatGPT do directly cite and evaluate sources. And try to get it in the mindset of comparing and contrasting arguments for and against. And I find I must argue against its points to see how it reacts.

More here https://softwaredoug.com/blog/2025/08/19/researching-with-ag...

I tried to use perplexity to find ideal settings for my monitor, it responded with concise list of distinct settings and why. When I investigated the source it was just people guessing and arguing with each other in the Samsung forums, no official or even backed up information.

I'd love if it had a confidence rating based on the sources it found or something, but I imagine that would be really difficult to get right.

FWIW GPT-5 (and o3, etc.) is one of the most critical-minded LLMs out there.

If you ask for information which is e.g. academic or technical it would cite information and compare different results, etc, without any extra prompt or reminder.

Grok 4 (at the initial release) was just reporting information in the articles it found without any analysis.

Claude Opus 4 also seems bad: I asked it to give a list of JS libraries of a certain kind in deep research mode, and it returned a document focused on market share and usage statistics. Looks like it stumbled upon some articles of that kind and got carried away by it. Quite bizarre.

So GPT-5 is really good in comparison. Maybe not perfect in all situations, but perhaps better than an average human

How are we feeling about the usage of the word research to indicate feature sets in LLMs? Is it truly representative of research? How does it compare to the colloquial “do your research” refrain used often during US election years?

> I’ve found ChatGPT and other LLMS can struggle to evaluate evidence - to understand the biases behind sources - ie taking data from a sketchy think tank as gospel.

This is what I keep finding, it mostly repeats surface level "common knowledge." It usually take a few back and forths to get to whether or not something is actually true - asking for the numbers, asking for the sources, asking for the excerpt from the sources where they actually provide that information, verifying to make sure it's not hallucinating, etc. A lot of the time, it turns out its initial response was completely wrong.

I imagine most people just take the initial (often wrong) response at face value, though, especially since it tends to repeat what most people already believe.

Yeah trying to make well-researched buying decisions for example is really hard because you'll just quite a lot of opinions dominated by marketing material, which aren't well counterbalanced by the sort of angry Reddit posts or YouTube comments I'd often treat as red flags.

> I always ask ChatGPT do directly cite and evaluate sources. And try to get it in the mindset of comparing and contrasting arguments for and against. And I find I must argue against its points to see how it reacts.

Same here. But it often produces broken or bogus links.

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