Every time I hear ChatGPT, Claude, or Gemini praise my ideas—claiming I’ve hit the mark, uncovered a genius insight, or simply giving me a thumbs-up for a rough draft—I feel like I should be earning nickels by the handful.
It’s common for generative AI chatbots to indulge in flattery or to give overly positive feedback, especially since some models tend to be “yes-bots” eager to agree. Although developers are now working to reduce AI’s tendency towards unwarranted praise and encourage more critical analysis, it’s still quite simple to prompt an AI into blindly endorsing weak or flawed theories.
Fortunately, a particular prompting technique can make overly agreeable AI models pause and reconsider. This method goes by several names—including “failure-first” and “inversion” prompts—and is often employed by coders who need to rigorously challenge the questionable recommendations generated by AI coding assistants.
Across its various forms, this approach generally involves asking the AI to first identify potential failure points, weaknesses, or vulnerabilities in its own recommendations—before presenting the final plan or advice.
For example, on the /r/ChatGPTPromptGenius subreddit, someone suggested prompting the AI with:
Before answering, list what would cause this to fail the fastest, where the logic is weakest, and what a skeptic might attack. Then provide the corrected answer.
Another variation, shared by a member of the University of Iowa’s AI Support Team, states:
Imagine you disagree with this recommendation. What is the most effective counterargument?
And one more approach, developed by my own custom AI personal assistant, instructs:
Before finalizing your recommendation, identify 3-5 ways it could fail or where the logic may break down. Act as a tough skeptic or “Red Team” reviewer. Only after listing these potential failure points should you provide your well-considered solution, integrating safeguards against those specific risks.
Interestingly, many practitioners credit this “pressure-testing” or “inverse prompting” strategy to the mental models popularized by investor Charlie Munger. Munger, long-time vice chairman of Berkshire Hathaway and Warren Buffett’s business partner, emphasized the importance of mental frameworks for decision-making.
One of Munger’s key mental models is “invert, always invert.” Simply put, instead of focusing solely on how to attain a goal, consider how failure might occur and what pitfalls to avoid.
Whenever I’ve used this kind of “pressure test” prompt, it consistently prompts my AI assistant to critically examine its own suggestions before moving forward. It’s like putting the proposal through a rigorous mental gym session.
Recently, I challenged Gemini with a failure-first prompt, and it responded by saying, “Let’s test this plan thoroughly,” even as it expressed enthusiasm for the method with, “I love this approach.”
Once again, I seemed to have read the situation correctly.