To gauge an expert's (human or AI) true depth, go beyond recall-based questions. Pose a complex problem with multiple constraints, like a skeptical audience, high anxiety, and a tight deadline. A genuine expert will synthesize concepts and address all layers of the problem, whereas a novice will give generic advice.
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When hiring, top firms like McKinsey value a candidate's ability to articulate a deliberate, logical problem-solving process as much as their past successes. Having a structured method shows you can reliably tackle novel challenges, whereas simply pointing to past wins might suggest luck or context-specific success.
To get beyond generic advice, instruct ChatGPT's voice mode to act as a challenging mentor. Prime it with a specific framework like the Theory of Constraints (TOC) and provide your resource limitations. This structured dialogue forces the AI to challenge your assumptions and generate realistic, actionable solutions instead of pleasantries.
In a world of AI-generated content, true expertise is proven by the ability to answer spontaneous, unscripted questions on a topic for an extended period. This demonstrates a level of domain mastery and authenticity that AI cannot replicate, building genuine trust with an audience.
For high-level leadership roles, skip hypothetical case studies. Instead, present candidates with your company's actual, current problems. The worst-case scenario is free, high-quality consulting. The best case is finding someone who can not only devise a solution but also implement it, making the interview process far more valuable.
Ineffective interviews try to catch candidates failing. A better approach models a collaborative rally: see how they handle challenging questions and if they can return the ball effectively. The goal is to simulate real-world problem-solving, not just grill them under pressure.
To get higher-quality input from busy medical experts, use specialized AI tools like Consensus.app to review scientific literature first. Then, present your tentative conclusions to the professional, demonstrating you've done the preliminary work, which encourages a more thoughtful and detailed response.
The most effective way to build a powerful automation prompt is to interview a human expert, document their step-by-step process and decision criteria, and translate that knowledge directly into the AI's instructions. Don't invent; document and translate.
Instead of generic benchmarks, Superhuman tests its AI models against specific problem "dimensions" like deep search and date comprehension. It uses "canonical queries," including extreme edge cases from its CEO, to ensure high quality on tasks that matter most to demanding users.
Standard AI models are often overly supportive. To get genuine, valuable feedback, explicitly instruct your AI to act as a critical thought partner. Use prompts like "push back on things" and "feel free to challenge me" to break the AI's default agreeableness and turn it into a true sparring partner.
Traditional hiring assessments that ban modern tools are obsolete. A better approach is to give candidates access to AI tools and ask them to complete a complex task in an hour. This tests their ability to leverage technology for productivity, not their ability to memorize information.
