@jakevin7: Sharing a god-tier review prompt methodology. The LLM self-correction survey 'When Can LLMs Actually Correct Their Own Mistakes?' concludes that without reliable external feedback such as test results or tool outputs, a model relying only on self-reflection often cannot steadily correct errors...

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Summary

Shared a prompt methodology based on LLM self-correction research, emphasizing that self-checking is limited without external feedback, and recommending progressively enhanced prompting strategies such as adversarial review.

Sharing a god-tier review prompt methodology. The LLM self-correction survey 'When Can LLMs Actually Correct Their Own Mistakes?' concludes that without reliable external feedback such as test results or tool outputs, models relying only on self-reflection usually struggle to reliably correct errors. In other words, simply "asking the model to check itself" has limited effectiveness. The following three prompts tend to increase in effectiveness step by step: ● "Do a review." ● "Conduct an adversarial review." ● "Focus on checking the error-prone aspects of XX, adversarial review." Essentially, a human acts as a guide, providing the Agent with extra information + methodology. Adversarial review is the methodology for review, and XX error-prone aspects are the extra information. These all give the Agent new attention directions and additional computational budget.
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Cached at: 08/06/26, 12:42 PM

Sharing a top-tier review prompt methodology.

The survey on LLM self-correction, When Can LLMs Actually Correct Their Own Mistakes?, concludes that without reliable external feedback such as test results or tool outputs, models relying solely on their own reflection often struggle to reliably correct errors. In other words, simply asking the model to “check itself” has very limited effectiveness.

The following three prompts tend to be progressively more effective:

  • “Review it.”
  • “Perform an adversarial review.”
  • “Focus on checking the XX error-prone aspects, and conduct an adversarial review.”

Essentially, the human acts as a guide, providing the Agent with additional information + methodology. The adversarial review is the methodology for the Review, and “XX error-prone aspects” is the additional information. Both of these give the Agent new attention directions and extra computational budget.

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