Why it’s a mistake to ask chatbots about their mistakes
Ars Technica
Why it’s a mistake to ask chatbots about their mistakes
The tendency to ask AI bots to explain themselves reveals widespread misconceptions about how they work.
> # The only thing I know is that I know nothing
A friend of mine sent this article along with what he calls chatbots.
"Statistical text generators"
Its a good descriptor of what is happening in AI chatbots.
This article addresses a pattern I have seen with people using chatbots and it signals they do not know what is going on inside the machine. And why would they? We've been deceived by those selling them as artificial intelligence. Calling these tools simulated intelligence is much more helpful. They simulate intelligence. Much like a picture of a person is not a person, an AI chatbot is not a mind.
> When something goes wrong with an AI assistant, our instinct is to ask it directly: "What happened?" or "Why did you do that?" It's a natural impulse—after all, if a human makes a mistake, we ask them to explain. But with AI models, this approach rarely works, and the urge to ask reveals a fundamental misunderstanding of what these systems are and how they operate.
> The first problem is conceptual: You're not talking to a consistent personality, person, or entity when you interact with ChatGPT, Claude, Grok, or Replit. These names suggest individual agents with self-knowledge, but that's an illusion created by the conversational interface. What you're actually doing is guiding a statistical text generator to produce outputs based on your prompts.
## Chatbots aren't Singular
> There is no consistent "ChatGPT" to interrogate about its mistakes, no singular "Grok" entity that can tell you why it failed, no fixed "Replit" persona that knows whether database rollbacks are possible. You're interacting with a system that generates plausible-sounding text based on patterns in its training data (usually trained months or years ago), not an entity with genuine self-awareness or system knowledge that has been reading everything about itself and somehow remembering it.
Once an AI language model is trained (which is a laborious, energy-intensive process), its foundational "knowledge" about the world is baked into its neural network and is rarely modified. Any external information comes from a prompt supplied by the chatbot host (such as xAI or OpenAI), the user, or a software tool the AI model uses to retrieve external information on the fly.
## AI Chatbots Do Not Know Anything
> Unlike humans who can introspect and assess their own knowledge, AI models don't have a stable, accessible knowledge base they can query. What they "know" only manifests as continuations of specific prompts. Different prompts act like different addresses, pointing to different—and sometimes contradictory—parts of their training data, stored as statistical weights in neural networks.
> This means the same model can give completely different assessments of its own capabilities depending on how you phrase your question. Ask "Can you write Python code?" and you might get an enthusiastic yes. Ask "What are your limitations in Python coding?" and you might get a list of things the model claims it cannot do—even if it regularly does them successfully.
> The randomness inherent in AI text generation compounds this problem. Even with identical prompts, an AI model might give slightly different responses about its own capabilities each time you ask.
## Agentic AI Compounds These Issues
> Even if a language model somehow had perfect knowledge of its own workings, other layers of AI chatbot applications might be completely opaque. For example, modern AI assistants like ChatGPT aren't single models but orchestrated systems of multiple AI models working together, each largely "unaware" of the others' existence or capabilities. For instance, OpenAI uses separate moderation layer models whose operations are completely separate from the underlying language models generating the base text.
> When you ask ChatGPT about its capabilities, the language model generating the response has no knowledge of what the moderation layer might block, what tools might be available in the broader system, or what post-processing might occur. It's like asking one department in a company about the capabilities of a department it has never interacted with.
## Fake It Till You Make It on GPUs
> A lifetime of hearing humans explain their actions and thought processes has led us to believe that these kinds of written explanations must have some level of self-knowledge behind them. That's just not true with LLMs that are merely mimicking those kinds of text patterns to guess at their own capabilities and flaws.
Stacker News
Why it’s a mistake to ask chatbots about their mistakes \ stacker news
The only thing I know is that I know nothing A friend of mine sent this article along with what he calls chatbots. "Statistical text generators" It...
Guys, its important to understand AI. What it is, how it works, and most importantly what it isn't.
This guy was dumb but I read stuff from people that seem very intelligent everyday that shows me they don't get AI and are buying what Scam Altman is selling. He's a liar. Chatbots are tech prediction machines. They don't know anything.
