Frame your interaction with AI as if you're onboarding a new employee. Providing deep context, clear expectations, and even a mental "salary" forces you to take the task seriously, leading to vastly superior outputs compared to casual prompting.

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.

AI tools rarely produce perfect results initially. The user's critical role is to serve as a creative director, not just an operator. This means iteratively refining prompts, demanding better scripts, and correcting logical flaws in the output to avoid generic, low-quality content.

Treat your AI like a brilliant intern who has raw talent but lacks experience and memory. This mental model encourages providing clear instructions and assuming best intentions while being prepared to constantly remind it of past decisions and project constraints, preventing it from making repeated, simple mistakes.

Achieve higher-quality results by using an AI to first generate an outline or plan. Then, refine that plan with follow-up prompts before asking for the final execution. This course-corrects early and avoids wasted time on flawed one-shot outputs, ultimately saving time.

Each generative step in an AI workflow introduces potential degradation or 'lossiness'. Chaining multiple steps together without checks—like asking AI to find a value prop, then an ICP, then write an email—compounds errors and produces generic, ineffective output. It's crucial to be thoughtful about workflow design and human-in-the-loop review.

AI can easily generate content that satisfies process requirements but lacks real value ("work slop"). This is less of a problem in outcome-focused cultures where work is measured against customer-centric KPIs, not in process-driven ones that just reward completing tasks.

Instead of perfecting a single prompt, treat AI interaction as a rapid, iterative cycle. View the first output as a draft. Like managing an employee, provide feedback and refine the result over several short cycles to achieve a superior outcome, which is more effective than front-loading all effort.

Contrary to the goal of full automation, the most effective AI workflows intentionally preserve points of friction. These moments—where a human must intervene, check intent, or re-steer the process—are crucial for maintaining control and ensuring the output aligns with strategic goals, preventing the system from running unchecked in the wrong direction.