For one month beginning on October 5, I ran an experiment: Every day, I asked ChatGPT 5 (more precisely, its “Extended Thinking” version) to find an error in “Today’s featured article”. In 28 of these 31 featured articles (90%), ChatGPT identified what I considered a valid error, often several. I have so far corrected 35 such errors.
legitimate use of a LLM
Yep. Let it flag potential problems, and have humans react to it, e.g. by reviewing and correcting things manually. AI can do a lot of things quick and efficiently, but it must be supervised like a toddler.
I find that an extremely simplified way of finding out whether the use of an LLM is good or not is whether the output from it is used as a finished product or not. Here the human uses it to identify possible errors and then verify the LLM output before acting and the use of AI isn’t mentioned at all for the corrections.
The only danger I see is that errors the LLM didn’t find will continue to go undiscovered, but they probably would be undiscovered without the use of the LLM too.
Or it flags something as an error falsely and the human has so much faith in the system that it must be correct, and either wastes time finding the solution or bends reality to “correct” it in a human form of hallucinating bs. Especially dangerous if saying there is an error supports the individual’s personal beliefs
Edit:
I’ll call it “AI-induced confirmation bias” cousin to AI-induced psychosis.
I think the first part you wrote is a bit hard to parse but I think this is related:
I think the problematic part of most genAI use cases is validation at the end. If you’re doing something that has a large amount of exploration but a small amount of validation, like this, then it’s useful.
A friend was using it to learn the linux command line, that can be framed as having a single command at the end that you copy, paste and validate. That isn’t perfect because the explanation could still be off and it wouldn’t be validated but I think it’s still a better use case than most.
If you’re asking for the grand unifying theory of gravity then:
Yeah, my morning brain was trying to say that when it is used as a tool by someone that can validate the output and act upon it then it’s often good. When it is used by someone who can’t, or won’t, validate the output and simply uses it as the finished product then it usually isn’t any good.
Regarding your friend learning to use the terminal I’d still recommend validating the output before using it. If it’s asking genAI about flags for ls then sure no big deal, but if a genAI ends up switching around sda and sdb in your dd command resulting in a wiped drive you only got yourself to blame for not checking the manual.
“AI” summed up. 95% of the time it’s pointless bullshit being shoehorned into absolutely everything. 5% of the time it can be useful.
like Comic Sans
Something weird about corporations spending billions on “the Comic Sans of technology”
Yes and no. I have enjoyed reading through this approach, but it seems like a slippery slope from this to “vibe knowledge” where LLMs are used for actually trying to add / infer information.
Don’t discard a good technique cause it can be implemented poorly.