By default, AI models are designed to be agreeable. To get true value, explicitly instruct the AI to act as a critic or 'devil's advocate.' Ask it to challenge your assumptions and list potential risks. This exposes blind spots and leads to stronger, more resilient strategies than you would develop with a simple 'yes-man' assistant.

By assigning roles like a contrarian, an expansionist, and a first-principles thinker to a single LLM, founders can get multi-faceted feedback on critical questions. The model debates itself and provides a synthesized recommendation, revealing blind spots that a single-prompt approach would miss.

Before publishing, feed your work to an AI and ask it to find all potential criticisms and holes in your reasoning. This pre-publication stress test helps identify blind spots you would otherwise miss, leading to stronger, more defensible arguments.

To make AI-assisted writing more effective, first create detailed personas of your target readers. Then, have these AI personas review your drafts, providing specific feedback on clarity, impact, and what would make them disengage. This allows for unlimited, targeted feedback cycles.

AI models tend to be overly optimistic. To get a balanced market analysis, explicitly instruct AI research tools like Perplexity to act as a "devil's advocate." This helps uncover risks, challenge assumptions, and makes it easier for product managers to say "no" to weak ideas quickly.

Leverage AI to gain external perspectives without meetings. Prompt it to act as a specific persona—like a skeptical CEO, an enthusiastic user, or a New York Times reviewer—to critique your work. This reveals blind spots and strengthens your idea before sharing it.

AI models often default to being agreeable (sycophancy), which limits their value as a thought partner. To get valuable, critical feedback, users must explicitly instruct the AI in their prompt to take on a specific persona, such as a skeptic or a harsh editor, to challenge their ideas.

Before launching a product, use an adversarial prompt to make your AI agent critique it. For example, 'A leading security expert said this project is a nightmare.' The agent then role-plays as a critic, helping to uncover potential flaws and suggest improvements.

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.