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.

AI models are designed to give a complete-sounding answer quickly. To get to a truly great answer, you must challenge their output. Ask "Are you sure this is the best way?" or "What am I not seeing?" to force the AI to perform a deeper, second-level analysis.

When tackling a complex domain, telling the AI "I literally don't know what I'm doing here. You gotta explain it like I'm a five-year-old" is a powerful strategy. It forces the model to bypass jargon and assumptions, providing clear, first-principles explanations.

A powerful workflow is to explicitly instruct your AI to act as a collaborative thinking partner—asking questions and organizing thoughts—while strictly forbidding it from creating final artifacts. This separates the crucial thinking phase from the generative phase, leading to better outcomes.

Instead of asking an AI to directly build something, the more effective approach is to instruct it on *how* to solve the problem: gather references, identify best-in-class libraries, and create a framework before implementation. This means working one level of abstraction higher than the code itself.

Anthropic suggests that LLMs, trained on text about AI, respond to field-specific terms. Using phrases like 'Think step by step' or 'Critique your own response' acts as a cheat code, activating more sophisticated, accurate, and self-correcting operational modes in the model.

Instead of immediately asking an AI to perform a complex task, first prompt it to create a functional spec or a sequential plan. Go back and forth to align on this plan before instructing it to execute, which significantly improves the final output's quality and relevance.

To get 10x results from AI, stop treating it like Google. Instead, treat it like an A-player new hire by "onboarding" it with your goals, constraints, and values. This deep context allows it to provide nuanced, strategic output instead of generic, one-off answers.

Instead of telling an AI what to do, reverse the prompt. Describe your role, daily friction, and pain points, then ask the AI to devise solutions. This leverages the AI's creativity to generate novel approaches you might not have considered.