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Enterprise software. Since 1993.
I think many of us are using LLMs the wrong way.
Not because the tools are bad. Because we are getting trapped in an endless review loop.
You create something using ChatGPT. Then you ask Claude to review it.
Claude says: “Good start, but this is 6/10.”
So you ask Claude to fix it. Then you bring it back to ChatGPT.
ChatGPT says: “Better. Maybe 7.5/10, but still needs work.”
So you ask ChatGPT to fix it again. Then Claude reviews it again and says: “Now it is 8/10.”
Still not 10/10. And the loop continues.
Review. Roast. Rewrite. Review again.
This can happen inside a single tool. You can write something in one ChatGPT session, open another ChatGPT session, ask for a brutal review, and suddenly the same tool finds new issues.
Why?
Because every review prompt creates a new judge.
The model is not always reviewing against your actual goal. It is often reviewing against the criteria it assumes in that moment.
At some point, the content is not necessarily getting better. It is just getting different.
This is where AI starts becoming a bottleneck instead of a productivity tool.
The mistake is chasing 10/10 from an LLM.
A score from AI is not an absolute truth. It is a generated opinion based on the prompt, model behavior, and assumed criteria.
The better question is not: “How do I make this 10/10?”
The better question is: “Is this good enough for the audience, purpose, and outcome?”
Stop asking for open-ended reviews.
Don’t ask: “Review this and score it.”
Ask: “Review this only for clarity, relevance to the audience, and missing business context.”
Even better, define the success criteria first. For example:
“This post is for business leaders using LLMs for writing and review. The goal is to explain why AI review loops happen and how to avoid them. Review only against this goal.”
That changes everything.
LLMs should help improve work. They should not become the final approval authority.
Because the goal is not to make ChatGPT happy. The goal is not to make Claude happy.
The goal is to make the work useful.
Sometimes the next AI review does not improve the work. It only moves the finish line.