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.

Treating AI coding tools like an asynchronous junior engineer, rather than a synchronous pair programmer, sets correct expectations. This allows users to delegate tasks, go to meetings, and check in later, enabling true multi-threading of work without the need to babysit the tool.

People struggle with AI prompts because the model lacks background on their goals and progress. The solution is 'Context Engineering': creating an environment where the AI continuously accumulates user-specific information, materials, and intent, reducing the need for constant prompt tweaking.

The key skill for using AI isn't just prompting, but "context engineering": framing a problem with enough context to be solvable. Shopify's CEO found that mastering this skill made him a better communicator with his team, revealing how much is left unsaid in typical instructions.

To successfully implement AI, approach it like onboarding a new team member, not just plugging in software. It requires initial setup, training on your specific processes, and ongoing feedback to improve its performance. This 'labor mindset' demystifies the technology and sets realistic expectations for achieving high efficacy.

Getting a useful result from AI is a dialogue, not a single command. An initial prompt often yields an unusable output. Success requires analyzing the failure and providing a more specific, refined prompt, much like giving an employee clearer instructions to get the desired outcome.

To effectively leverage AI, treat it as a new team member. Take its suggestions seriously and give it the best opportunity to contribute. However, just like with a human colleague, you must apply a critical filter, question its output, and ultimately remain accountable for the final result.

Don't view AI tools as just software; treat them like junior team members. Apply management principles: 'hire' the right model for the job (People), define how it should work through structured prompts (Process), and give it a clear, narrow goal (Purpose). This mental model maximizes their effectiveness.

The most effective AI user experiences are skeuomorphic, emulating real-world human interactions. Design an AI onboarding process like you would hire a personal assistant: start with small tasks, verify their work to build trust, and then grant more autonomy and context over time.