Before delegating a complex task, use a simple prompt to have a context-aware system generate a more detailed and effective prompt. This "prompt-for-a-prompt" workflow adds necessary detail and structure, significantly improving the agent's success rate and saving rework.

Don't ask an AI agent to build an entire product at once. Structure your plan as a series of features. For each step, have the AI build the feature, then immediately write a test for it. The AI should only proceed to the next feature once the current one passes its test.

After an initial analysis, use a "stress-testing" prompt that forces the LLM to verify its own findings, check for contradictions, and correct its mistakes. This verification step is crucial for building confidence in the AI's output and creating bulletproof insights.

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.

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.

Instead of manually crafting complex instructions, first iterate with an AI until you achieve the perfect output. Then, provide that output back to the AI and ask it to write the 'system prompt' that would have generated it. This reverse-engineering process creates reusable, high-quality instructions for consistent 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 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.

For complex, one-time tasks like a code migration, don't just ask AI to write a script. Instead, have it build a disposable tool—a "jig" or "command center”—that visualizes the process and guides you through each step. This provides more control and understanding than a black-box script.