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.
This is crazy lol they obviously have access to information.
As I've mentioned elsewhere, not if by "information" you mean semantic content that a mind can process. What they have are vector fields (essentially just numbers) with statistically more or less likely relationships.
If I say, "take me out to the ballgame" to an LLM, the tokens representing the words in the next verse of the song are statistically "close" in the vector database, so it's likely to generate them. But that doesn't mean it actually knows the lyrics... or even has those lyrics recorded in a regular database anywhere.
That's why they hallucinate. The model determines that the next token is something nonsensical, but it has no way of understanding that it has made a mistake. In a sense, it actually hasn't made a mistake. It's done exactly what it's designed to do. It's just that in the case of hallucinations, its output isn't useful.
Is that required?
Do you have those lyrics recorded in a regular database in your head?
Of course, LLMs are more "human" if they occasionally mis-remember the lyrics...
You are both right. An LLM inherently has access to stuff the same way a brain in a jar has access to stuff. It's information comes from fine-tuning the models to return syntax that agent code can interpret as a request to invoke a tool. That tool returns information to the context of the conversation. It doesn't learn and it can't truly remember things. Every time you start a session it is brand new. It sees your codebase for the first time every time.
The information access they have is whatever the agent allows it to access via tool exposure. Be it built in tools, or MCP servers
Eternal sunshine of the spotless mind... it has its advantages.
I have mine develop and maintain a set of documentation to introduce fresh agents to the project efficiently and correctly.