this post was submitted on 22 Aug 2026
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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.
Well, vector fields are information. But they have no understanding. The number 1 might be followed by 2 in 99.999% of cases, but it has no function to explain why, or to contextualize a scenario where that might be wrong.
Actually, the many dimensions of the vector field are exactly where and how they do this.
Google Gemini prompt: "List common situatuions where 2 would not be expected to folow 1"
Here are common situations where the number 2 would not be expected to follow the number 1:
🔢 Alternative Number Systems
🏷️ Identifiers and Classifications
🕒 Time and Measurements
🃏 Games and Sports
🗣️ Linguistics and Formats
To help me narrow down what you are looking for, could you tell me if you need this for a mathematical logic puzzle, a programming algorithm, or a creative writing project?
Those context questions it asked at the end are a prompt to you to feed it dimensional guidance into its vector fields for more specifically applicable responses to your vague and open ended example.