Get better results from AI coding tools by treating them like a new hire. Provide a clear strategy document or PRD as "long-term context" or "project memory." This initial onboarding helps the AI understand the project's goals, leading to more accurate and coherent builds.

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.

The era of giving AI simple, discrete tasks like "write a blog post" is ending. To effectively use emerging agentic AI teams, you must shift to providing high-level outcomes, such as "develop a content strategy to grow our audience by 30%," and let the AI orchestrate the necessary steps.

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.

To get a thorough implementation plan from Codex, provide it with a `plans.md` file. This file acts as a template, or "meta-plan," defining what a good plan looks like (e.g., milestones, self-contained steps), which guides the AI to produce a more structured output.

Instead of basic prompting, use an AI agent's "plan mode" to collaboratively outline a complex task, like writing a strategy doc. This lets you align on structure, sources, and verification steps before generation, yielding far superior results. It's like briefing a junior employee.

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.

Instead of detailing every step, state your high-level goal and instruct the AI to ask clarifying questions it needs to build the plan. This "reverse prompting" leverages the AI's reasoning to create a more robust solution than you could manually specify, which is a key advancement in AI interaction.