Instead of asking AI for answers, command it to ask you questions. Use the "Context, Role, Interview, Task" (CRIT) framework to turn AI into a thought partner. The "Interview" step, where AI probes for deeper context, is the key to generating non-obvious, high-value strategies.

A powerful, underutilized way to use conversational AI for learning is to ask it to quiz you on a topic after explaining it. This shifts the interaction from passive information consumption to active recall and reinforcement, much like a patient personal tutor, solidifying your understanding of complex subjects.

Instead of simply commanding an AI, a team first instructed it to ask clarifying questions about their company's mission and selection criteria for podcast guests. This "interview" step forced the AI to understand deep context before generating outputs, leading to a much more effective and customized database of ideas.

Instead of manually writing personal context files, engage an AI in an "interview to draft to revision" loop. By having the AI ask targeted questions, you can more effectively surface and articulate the tacit knowledge about your roles, preferences, and processes that you wouldn't think to write down otherwise.

Instead of trying to craft a perfect, detailed prompt, Andrew Wilkinson tells the AI his high-level goal and instructs it to "ask me a shitload of questions to determine your prompt." The AI then conducts a 5-10 minute interview, gathering all necessary context to produce a superior result.

Instead of brainstorming alone, the system uses an AI "interview panel" with personas like Tim Ferriss and Joe Rogan. These AI interviewers ask probing questions about a chosen topic to extract detailed stories, specific examples, and nuanced viewpoints from the user. This turns a solo writing process into a structured, conversational extraction of expertise.

Instead of providing a detailed but potentially incomplete prompt, ask the AI model to interview you about your goal. This meta-prompting technique forces clarification and helps uncover "unknown unknowns" you hadn't considered, leading to a much better final output.

Instead of asking an AI to solve a problem directly, start by dumping your entire idea into the tool. Then, prompt the AI to act as an interviewer, asking clarifying questions. This iterative process helps refine the concept and uncovers hidden requirements, turning the AI into a true brainstorming partner rather than just a code generator.

Instead of just giving AI a task, command it to interview you first. By having the AI ask clarifying questions about assumptions, context, and potential gaps, you can surface your own unknown unknowns and provide the necessary context for a high-quality output.