this post was submitted on 22 Aug 2026
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I had this experience once. We have a ChatGPT license where I work, and I asked it to configure a switch that I wasn't familiar with. I simply described in words what network architecture I wanted and it did it! It even made some nice-looking documentation.
But, then I tried the new configs, and they didn't work. It turns out there were some key syntax things it got wrong. And the documentation was wrong on top of that, with incorrect diagrams, and when I asked it to fix it it made different errors I the diagrams in different places. On balance, I still saved some time over reading all the manuals and figuring out the syntaxes myself, but only because I made my own documentation with the results that worked. If I had trusted the AI I would be sunk.
I've concluded that AI gives the illusion of competence, like a overly confident new manager. This can be very attractive to a less experienced person. But it's really guessing, just like we all are. It can just guess after actually "reading" all the manuals. I haven't used AI to write anything more than simple configurations and helper scripts. If I did want to use AI for more it would be in more of a pair-programming context. I might have a window open where I describe some things and ask for analysis, but I wouldn't just run anything it does blindly.
Considering the vast amounts of knowledge it has at its disposal, I can only conclude that it’s not very smart at applying it. A person with a fraction of that knowledge will produce better results.
So it has more access to information, but the results are poor compared to a person.
The important thing to remember is that it actually has zero access to information, because that's not how LLMs work.
At their core, they're vector databases, and they're trying to probabilistically come up with the next most likely token in a stream of tokens found in the DB. You can manipulate the stream by injecting text such as the content of existing files (which becomes more tokens) into the stream, but it never actually understands any of it.
That's why hallucinations are inherently unavoidable. It's really all just hallucinations. It's just that you can sometimes get useful text from their hallucinations if they happen to comport with reality.
Ypu have no idea what youre talking about. They absolutely have access to "information"
No, they really don't. That's not how they work. At least, not if the "information" you're talking about is real semantic content that real minds can process.
Every piece of information you think an LLM has access to is actually just converted into a stream of additional tokens that are fed into the model to (hopefully usefully) modify the next tokens it predicts. That's not the same thing as having actual access to information. Tokens are just numbers with statistically more (or less) likely relationships to each other.
I'm not trying to downplay LLMs. They're architecturally interesting and have genuine uses. I'm just trying to head off a bit of technical inaccuracy.
Absolutely correct and well said. Until you give it a tool to call a websearch (in my case SearxNG), I occasionally break it out (local 27B model) when a search is pulling lots of AI slop, Spy vs Spy style. I make it give me references and it usually indicates a bad search (XY problem)
Local, avoids consolidation of power & privacy leaks
Use in limited cases, avoids skill atrophy
Nice
LLMs don't. There are tools that can fetch new information and then gets fed into the model as more tokens, but that's just a special case of what kescusay is saying about injecting text.