this post was submitted on 07 Mar 2026
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Bad craftsman blames his tools is what I'd answer to this.
I agree anyone using an LLM is a bad craftsman, because they're using a hammer to drive in a nail.
All LLMs are using a tool for the wrong task then, in your opinion? So in the composite object of "LLM" what is the tool and what is the task?
The tool is "Language Learning Model" and the task is "Learn language and mimic human speech."
The task is not "Provide accurate information" or "write code" or "provide legal advice" or "Diagnose these symptoms" or "provide customer service" or "manage a database".
And a human's task, along with any other lifeform, is to survive and reproduce. In pursuit of that goal we have learned many different complex strategies and methods to achieve it, same with an llm.
Peoples tasks are also not to provide accurate information, write code, provide legal advice etc. If a person can earn a living, attract a mate and raise children by lying, writing bad code, giving shitty legal advice etc. they will. It takes external discipline to make sure agents don't follow those behaviors. For humans that discipline is provided by education, socialization, legal systems etc. For LLMs that discipline is provided by fine tuning, ie. The lying models get down rated while the more truthful models get boosted.
They all "lie" because they don't actually know a damn thing. Everything an LLM outputs is just a guess of what a human might do.
An LLM has a great deal of declarative knowledge. Eg. It knows that the first president of the US is George Washington. Like humans it has built up this knowledge through reinforcement, the more a fact is reinforced by external sources, the more you/ it knows it. Like with humans when it reaches the edge of its knowledge base it will guess. If I ask someone who the 4th president of the US was they may guess Monroe, that person isn't lying, it's just an area that hasn't been reinforced (studied) as much so they are making their best guess, LLMs do the same. That doesn't mean that person cannot and will not ever know the 4th president, it just means they need more reinforcement / training / studying.
Humans as well as LLMs have a declarative knowledge area with a lot of grey area between knowing and not knowing. It'd be like a spectrum starting on one end with stuff that has been reinforced many times by people with high authority, what is your name would probably be the furthest on one side, to another end with stuff you've never heard or heard from untrustworthy sources. LLMs may not have the other dimension of trustworthiness that people do but the humans training it will usually compensate that with more repetition from trustworthy sources, eg. They'll put 10 copies of the new York times and only one of younewsnow.com or whatever in the training data.
An LLM has no knowledge.
My calculator does not "know" that 2+2=4, it runs the code it has been programmed with which tells it to output 4. It has no knowledge or understanding of what it's being asked to do, it just does what it is programmed to do.
An LLM is programmed to guess what a human would say if asked who the 4th president of the United States was. It runs the code that was developed with the training data to output the most likely response. Is it true? Doesn't matter. All that matters is that it sounds like something a human would say.
I trust the knowledge of my calculator more, because it was designed to give factual correct responses.
How do you know that George Washington is the first president? You weren't around in 1784, you have no experiential knowledge, you only have declarative knowledge of it, you read it from a book or heard it from a person enough to repeat the fact when asked. You are guessing what your history teacher would have said in elementary school. Declaritive knowledge is just memory and repetition, and an LLM can do memory and repetition.
Whether an LLM can determine truth depends on your definition of truth. If truth can only be obtained from experience and reasoning from first principles then an LLM can't determine truth. Then a statement like George Washington was the first president can't be true then because you can't derive it from experience or first principles, you weren't there, no one alive was there. George Washington was the first president derives it's validity and truth from the consensus of trustworthy people who say it's true. An LLM can derive this sort of truth by determining the consensus of its training data assuming its training data is from trustworthy sources or the more trustworthy sources are more reinforced.
Of course someone who doesn't believe "truth" exists thinks LLMs are just fine. You have to not believe things can be true in order to find their output acceptable.
Every week I see a new post of an LLM being blantly wrong. LLMs said to add glue to pizza to make the cheese stick together.
"They have improved the models since then..." Last week the American military used "AI" and it targeted a school as a military structure. The models are full of shit, they just manually remove the blantly incorrect shit whenever they make the rounds, and there's always more blantly incorrect shit to be found.
It's "Large Language Model", and the point is in "Large" and that on really large datasets and well-selected attention dimensions set it's good at extrapolating language describing real world, thus extrapolating how real world events will be described. So the task is more of an oracle.
I agree that providing anything accurate is not the task. It's the opposite of the task, actually, all the usefulness of LLMs is in areas where you don't have a good enough model of the world, but need to make some assumptions.
Except for "diagnose these symptoms", with proper framework around it (only using it for flagging things, not for actually making decisions, things that have been discussed thousands of times) that's a valid task for them.
This sounds like someone who knows nothing about construction saying "building a house" is a valid task because they don't understand why using a hammer to drive in a screw would be incorrect or why it's even a problem. "The results are good enough right?"
You are writing pretentious nonsense, go someplace else.