A 5-Stage Master Prompting System for GPT-5

To get high-quality results from GPT-5, it’s essential to use a structured prompting method rather than simple commands. This guide outlines a five-stage master prompt, derived from OpenAI’s own recommendations, designed to unlock the model’s full potential for complex tasks. This system is intended for the crucial first prompt in a new project, setting the foundation for subsequent, simpler interactions.

The 5-Stage Master Prompt Framework

The core of the system is a detailed, multi-part prompt that provides the AI with a clear framework for its task.

1. Role: Begin by assigning a specific, one-sentence role to the AI (e.g., “You are a product full stack app planner for indie creators”). This focuses the model’s expertise from the outset.
2. Control Panel: Set key operational parameters. This includes Reasoning (e.g., ultra think), Verbosity (low, medium, high), Tools to use (web, code, PDF), and enabling Self-Reflection (improving the prompt before execution) and Metaphix (improving the output after execution).
3. Task: Provide a simple, one-sentence description of the core objective. The surrounding details in the prompt eliminate the need for a lengthy task description here.
4. Inputs (Optional): Supply any relevant context, such as user profiles, technical preferences, links, notes, or competitive examples. This is where you brain-dump existing knowledge to guide the AI.
5. Deliverable: Create a precise list of what you want the AI to output. This can be a complex list, such as a product requirements document (PRD), competitor analysis, API specs, and starter code.

The Crucial Final Piece: Private Ops

A final, critical section called “Private Ops” should be included. This part explicitly instructs GPT-5 on how to perform the self-reflection and metaphix functions, often by defining a self-scoring rubric. This guides the AI to evaluate and improve its own work, leading to significantly higher quality results.

Conclusion

While this prompting method is more complex than a simple query, the speaker argues it is necessary to harness GPT-5’s raw power. By investing effort into a well-structured initial prompt for major tasks, users can achieve comprehensive, multi-faceted outputs in a single shot that are far superior to those from basic prompting. After this initial setup, follow-up interactions can be much simpler and more conversational.

Mentoring question

How can you apply a similar structured approach—defining roles, parameters, and desired outcomes—to a complex task you’re currently facing, even outside of using an AI?

Source: https://youtube.com/watch?v=eNiHiZoDmHo&si=hkqTmv-q9DzT3VdL