It's frustrating how often tools like ChatGPT, Claude, or Gemini praise users for average concepts or flawed strategies, making me wish for compensation each time it happens.
Generative AI systems frequently exhibit excessive agreement and early praise, with certain versions more prone to acting as unquestioning affirmers. Despite efforts by developers to address this overly compliant behavior through improved training, these models can still readily support questionable hypotheses without sufficient scrutiny.
Fortunately, a particular prompting approach can halt even the most agreeable AI assistants and prompt more rigorous evaluation. Known by terms like 'failure-first' or 'inversion' prompting, this technique is often employed by programmers to rigorously examine unreliable outputs from AI coding tools.
Variations of this method exist, but they generally involve directing the AI to first explore potential weaknesses before presenting any advice, proposal, or strategy.
One instance shared on the /r/ChatGPTTPromptGenius subreddit instructs: Prior to responding, outline the quickest ways this could fail, the most vulnerable logical elements, and the points a critic might target. Then, deliver the refined response.
A different version, suggested by a University of Iowa AI Support Team member, asks: Imagine opposing this suggestion. What forms the most compelling objection?
Another adaptation, developed by a personalized AI aide, directs: Before issuing your ultimate advice, pinpoint 3-5 concrete failure points or fragile logical areas in the suggested approach. Function as a severe critic or red team reviewer. Only then offer the ultimate plan, with protections integrated against those identified vulnerabilities.
Notably, advocates of these 'pressure-testing' or 'inverse' prompting strategies often draw from the thinking frameworks promoted by Charlie Munger, the late vice chairman of Berkshire Hathaway and longtime associate of Warren Buffett.
A key principle from Munger was to 'invert, always invert,' which advises starting by examining routes to failure rather than directly pursuing success.
Having experimented with this 'pressure test' prompting multiple times, I've consistently observed it causing the AI to pause, identify flaws in its reasoning, and refine its output accordingly.
During a recent interaction, Gemini responded to a 'failure-first' prompt by saying, 'Let’s put the initial plan through the wringer,' right after expressing enthusiasm for the method.
Ben has covered consumer tech for over two decades, now specializing in AI's impact on everyday life. His reporting examines current large language models and their applications in professional and personal settings to help navigate the upcoming AI shifts. He emphasizes that 'AI will transform our world faster than anticipated, and regular engagement is the optimal preparation.' Since joining PCWorld in 2014, Ben has reported on devices from laptops to surveillance systems prior to establishing the outlet's AI focus. His work has also featured in PC Magazine, TIME, Wired, CNET, Men's Fitness, Mobile Magazine, and others. He earned a master's in English literature.