10/02/2026
🤖 Anyone here working on “Chegg Aristotle” AI training?
I’m curious to know if others are experiencing the same thing.
Training AI sounds exciting—until you actually try to make an advanced AI model fail. 😅
The challenge is no longer simply asking a difficult question.
You have to think like an adversarial evaluator:
🔹 Can the model misunderstand the question?
🔹 Can I create an ambiguity it cannot resolve?
🔹 Can I design an image that exposes a vision-reasoning error?
🔹 Can I construct a chemistry problem where the model's seemingly perfect reasoning breaks down?
🔹 Can I produce a genuinely valid failure rather than a question the AI simply solves?
And here comes the interesting part…
AI solves it.
You modify the prompt.
AI solves it again.
You make it harder.
AI still solves it.
You start questioning your own existence. 😂
The irony?
👉 The better the AI becomes, the harder—and sometimes more time-consuming—it becomes for the human evaluator to produce a successful task.
Yet the compensation per task doesn't necessarily reflect the amount of reasoning, experimentation, failed attempts, prompt engineering, and verification required to create a genuine AI failure.
So I have a question for everyone working on Chegg Aristotle or similar AI-training projects:
Are you experiencing the same gap between the difficulty of creating a successful task and the compensation for completing it?
I'd genuinely like to hear your experience.
Maybe the next AI-training benchmark shouldn't only measure:
“Can AI solve the problem?”
Maybe it should also ask:
“How much human expertise was required to create a problem that AI couldn't solve?” 🤔
Because sometimes, beating AI is much harder than training it.
— Chiranjeevi sir
🧪 Chemistry Teacher | Chemistry Educator
🤖 Chemistry Prompt Creator for LLMs
🎯 AI/LLM Evaluation & Training
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