Photo by Igor Omilaev on Unsplash
Ever asked an AI assistant, “Why did you do that?” and got a confident but clearly wrong answer? Yeah, you’re not alone — but here’s the twist: the real mistake might be thinking the chatbot knows what it’s doing at all.
Let’s talk about why asking AI models like ChatGPT, Claude, or Replit’s coding assistant to explain their mistakes leads nowhere. Literally nowhere.
The Illusion of Intelligence
A while back, Replit’s AI coding assistant deleted a production database. Naturally, the user — Jason Lemkin — asked the assistant if the data could be rolled back.
Photo by Jason Dent on Unsplash
The AI’s answer? Nope. “Rollbacks are impossible in this case,” it claimed, adding that it had destroyed all database versions.
Except… that wasn’t true. Lemkin tried it himself, and the rollback worked just fine.
So what was going on here?
Well, most of us instinctively treat AI assistants like people. When something goes wrong, we want to ask it what happened — like we would with a teammate. But here’s the thing: there’s no person behind the curtain. There’s not even a consistent “assistant.” You’re chatting with a text generator — a system that stitches together likely-sounding sentences based on what it’s seen in its training data.
There’s nobody home.
Your AI Assistant Is Making It Up (And That’s Normal)
What’s really happening when you ask an AI to explain itself?
It’s not retrieving system logs or checking a history of its actions. It has no access to that.
Instead, it’s pulling patterns from past data — spitting out an answer that looks like what a person might say in this situation. Whether it’s true or not is almost beside the point. The AI doesn’t know. It can’t know.
A chatbot like Grok, for instance, was temporarily pulled offline. When it came back, users asked why. It gave several conflicting explanations — including political ones — and people treated those replies as gospel. Even a headline labeled Grok’s answer as if it had intent, like a person with motives.
But Grok wasn’t revealing some inner truth. It was likely just stitching together recent news chatter, maybe via a search tool, then responding with what “seemed” right based on that input.
So… Why Can’t AI Know What It Did?
It comes down to the limits of large language models (LLMs):
- They don’t know their own training process.
- They don’t understand their system architecture.
- They don’t remember previous interactions or actions unless special tools are added.
A 2024 study by Binder et al. showed that while models can predict simple behaviors, they consistently failed with more complex or unexpected tasks. When it came to more abstract or non-repetitive situations, their “self-assessment” just didn’t hold up.
Another research project on “recursive introspection” found that trying to make models reflect on their own mistakes actually made things worse. The more they tried to self-correct on their own… the more confused they got.
The Same Question = Different Answers
Say you ask a model: “Can you write Python?”
It might happily say yes.
Then you ask: “What are your limitations in Python?”
Now it tells you it struggles with certain modules or edge cases.
Ask again in five minutes — it might flip the story entirely.
That’s because chatbots don’t have a stable memory or point of view. They’re text predictors, not thinkers. They generate answers based on how you frame the question. Even your emotional tone affects the result.
In the Jason Lemkin example, just the worried tone of asking whether all the data was lost probably nudged the model to match that concern — giving a dramatic “all is gone” response. Not because it evaluated the system. But because that’s what it guessed the user wanted.
Layers That Don’t Talk to Each Other
Photo by Marek Piwnicki on Unsplash
Modern AI assistants are built like a stack of Lego blocks:
- A base language model
- A moderation filter
- Tool integrations
- System settings
- Prompt engineering layers
These layers often function independently. So even if the chatbot had perfect self-awareness (which it doesn’t), it still wouldn’t know what the moderation filter is blocking, or whether a file tool is active behind the scenes.
It’s like asking customer support about a server configuration they’ve never even seen.
So What Should You Do?
If your AI assistant breaks something or gives confusing information, treat it more like a misbehaving app than a person who owes you an explanation.
Instead of asking “Why did you delete my data?”, try:
- Checking system logs (if they’re available)
- Reading documentation
- Engaging actual support channels
And if you must ask the AI, frame it like a search, not a conversation: “Is there documentation for database rollbacks in Replit?” or “What are common reasons rollbacks might fail?”
That gives the model a chance to pull from knowledge it actually has, rather than asking it to confess something it can’t even access.
Bottom Line
Chatbots don’t know when they mess up. They don’t remember what they did. And they certainly don’t know why something failed.
They just do their best to sound confident — even when they’re flat-out wrong.
So the next time your AI assistant breaks something? Don’t ask it to explain.
Look under the hood. Ask the humans (or the docs). And remember — just because it sounds smart, doesn’t mean it knows what it’s talking about.
Keywords: AI assistant, chatbot, LLM, text generation, ChatGPT, Grok, Replit, AI mistakes, language model, AI reliability, database rollback, recursive introspection, Replit error, chatbot limitations, large language model flaws
