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.

Continuously trying to correct a confused AI in a long conversation is often futile, as a 'poisoned' context can lead it astray. The most effective approach is to abandon the conversation, start a new one, and incorporate your learnings into a better initial prompt.

Conceptualize Large Language Models as capable interns. They excel at tasks that can be explained in 10-20 seconds but lack the context and planning ability for complex projects. The key constraint is whether you can clearly articulate the request to yourself and then to the machine.

To get the best results from AI, treat it like a virtual assistant you can have a dialogue with. Instead of focusing on the perfect single prompt, provide rich context about your goals and then engage in a back-and-forth conversation. This collaborative approach yields more nuanced and useful outputs.

To get consistent results from AI, use the "3 C's" framework: Clarity (the AI's role and your goal), Context (the bigger business picture), and Cues (supporting documents like brand guides). Most users fail by not providing enough cues.

Many AI tools expose the model's reasoning before generating an answer. Reading this internal monologue is a powerful debugging technique. It reveals how the AI is interpreting your instructions, allowing you to quickly identify misunderstandings and improve the clarity of your prompts for better results.

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.

When an AI tool fails, a common user mistake is to get stuck in a 'doom loop' by repeatedly using negative, low-context prompts like 'it's not working.' This is counterproductive. A better approach is to use a specific command or prompt that forces the AI to reflect and reset its approach.

When a prompt yields poor results, use a meta-prompting technique. Feed the failing prompt back to the AI, describe the incorrect output, specify the desired outcome, and explicitly grant it permission to rewrite, add, or delete. The AI will then debug and improve its own instructions.