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.

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.

Don't limit an AI agent to tasks you can already imagine. After providing full context on your work, ask it open-ended questions like, “How can you make my life easier?” This strategy of “hunting the unknown unknowns” allows the AI to suggest novel, high-value workflows you wouldn't have thought to request.

Evolve your interaction with AI from a manual, iterative prompting process to one of system design. The advanced approach is to architect 'agent loops' where you set a high-level goal and clear evaluation criteria, then allow the AI to iterate on its own. This reframes your role from active manager to systems architect.

To get better results from AI, don't ask for the final output immediately. Instead, prompt the AI to first provide a detailed process. This allows you to review and debug its logic, then instruct it to execute each step for a more accurate outcome.

A powerful but unintuitive AI development pattern is to give a model a vague goal and let it attempt a full implementation. This "throwaway" draft, with its mistakes and unexpected choices, provides crucial insights for writing a much more accurate plan for the final version.

The best AI results come from iterative refinement. After an initial build, continue conversing with the agent to tweak outputs. Tell it to adjust sentence structure or writing style and redeploy. This continuous feedback loop is key to improving performance.

A powerful technique for creating robust software plans is to use AI as an adversarial partner. After drafting a specification, prompt an AI to "tear it apart" by identifying underspecified or inconsistent points. Iterate on this process until the AI's feedback becomes niche, indicating a solid spec.

To get AI agents to perform complex tasks in existing code, a three-stage workflow is key. First, have the agent research and objectively document how the codebase works. Second, use that research to create a step-by-step implementation plan. Finally, execute the plan. This structured approach prevents the agent from wasting context on discovery during implementation.