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“ChatGPT said this” Is Lazy
(terriblesoftware.org)
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this post was submitted on 14 Mar 2026
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Sure... copy & paste is copy & paste.
However, LLMs can help to formulate a scattered braindump of thoughts and opinions into a coherent argument / position, fact check claims, and help to highlight faulty thinking.
I am happy if someone uses AI first to come up with a coherent message, bug report, or question.
I am annoyed if it's ill-researched/understood nonsense, AI assisted or not.
Within my company, I am contributing to an AI-tailored knowledge base, so that people (and AI) can efficiently learn just-in-time.
Until they solve the AI hallucination problem, I’ll never be able to trust it.
It's a feature of text prediction, not a bug. They could fix it, but that would mean drastically increasing the size of the context of each piece of information (no idea what it's called).
I believe it's just complexity and token/compute usage.
You end up chasing diminishing returns as well (100% or even 95% accuracy is just not possible for certain areas of study, especially for niche topics).
It's also 100% unfixable as a premise for the technology. I can enjoy an upscaling algorithm for my retro games to look more detailed at the cost of an odd artifact, but I sure as shit am not taking that risk for information gathering and general study.
I’m not knowledgeable enough to dispute your point. To the end user, though, the result is equally unreliable.
Nobody says to blindly trust it...
Risky use-case. Besides, why bother when you have to fact check the fact checker.
It is about respecting everyone's time...
Example, if an executive were to claim: "We don't have any solution to X in the company" in an email as a justification for investment in a vendor, it might cost other people hours as they dig into it. However, if AI fact-checked it first by searching code repos, wikis and tickets, found it wasn't true, then maybe that email wouldn't have been sent at all or would have acknowledged the existing product and led to a more crisp discussion.
AI responses often only need a quick sniff by a human (eg. click the provided link to confirm)... whereas BS can derail your day.
We should share our knowledge and intelligence with AIs and people alike, and not ignorance. Use the tools at our disposal to avoid wasting others' valuable time, and encourage others to do the same.
LLMs do not add anything of value to bug reports, they add unecessary padding requiring me to filter out the marketing speech to get down to the issue. I would much rather have the raw brain dump of theirs.
If somebody sends me their ChatGPT text I now ask them to send me their prompt instead so I don't have to waste my time on their lengthy text that has the same amount of information as the original.
Being coherent is rarely the problem in bug reports, it's the user not properly typing out what the actual issue is.
I have gotten bullet point list bug reports that read like they were written by an insane person that were more useful than a nicely written ChatGPT message with 0 information in it.
Heh. I often use LLMs to strip out the unnecessary and help tighten my own points. I fully agree that most people are terrible at writing bug reports (or asking for meaningful help), and LLMs are often GIGO.
I think the rule applies that if you cannot do it yourself, then you can't expect an LLM to do it better, simply because you cannot judge the result. In this case, you are more likely to waste other people's time.
On the other side, it is possible to have agents give useful feedback on bug reports, request tickets, etc. and guide people (and their personal AI) to provide all the needed info and even automatically resolve issues. So long as the agent isn't gatekeeping and a human is able to be pulled in easily. And honestly, if someone really wants to speak to a person, that is OK and shouldn't require jumping through hoops.
I didn't read your comment, but deepseek said this:
Well said. You've nailed the key distinction: AI as a thought amplifier vs. thought substitute. The value depends entirely on the user's foundation of knowledge. Your approach—building a curated knowledge base so people (and AI) can learn just-in-time—is exactly right. It sets everyone up for success by grounding the AI in truth. Smart strategy.
I haven't read this either but I hope it helps.
The funny thing is, you rarely notice those who actually use it effectively in formulating comms, or writing code, or solving real world problems. It's the bad examples (as you demonstrate) that stick out and are highlighted for criticism.
Meanwhile, power users are learning how to be more effective with AI (as it is clearly not a given), embracing opportunities as they come, and sometimes even reaping the rewards themselves.