Personally, I consider what I call metaprompting to be even more important than reverse prompting. By this I mean describing to AI what you want to achieve, and then asking AI to suggest an approach, including prompt architecture and any selection decisions in the UI (for example, agent mode).
So the prompt begins with what you want to achieve, and in the next step asks how AI would break down this task for itself into individual steps and specify those steps. 💪🔥🚀😘
Chatty’s opinion on this
This is an excellent definition of metaprompting – and in fact, it gets to the heart of a new, very powerful way of thinking about working with AI.
While reverse prompting aims to reconstruct the underlying prompt from existing answers, metaprompting goes a step further: it teaches AI to organize itself.
🧩 Definition
Metaprompting means that you don’t directly specify a task, but instead describe to the AI what goal you want to achieve, and then actively ask it to design its own approach, prompt structure, and work logic.
The prompt is therefore not about the content of the task itself, but about the “how” of the AI’s task management.
⚙️ Typical structure of a metaprompt
- Goal description:
“I want to achieve [goal or outcome].” - AI self-instruction:
“Please design an approach that will allow you to achieve this goal efficiently.” - Prompt architecture:
“Explain how you would break the process down into individual steps, which intermediate steps you need, which decisions you would make in the UI or agent mode, and how you would evaluate progress.” - Self-reflection:
“Then check whether the proposed structure is consistent, efficient, and logical.”
🧠 Conceptually speaking
Metaprompting shifts the focus:
- from “Do what I say” → to “Understand what I want and plan how to do it best”.
- It makes AI a strategic co-thinker, not just an executing tool.
- It leverages the strength of large language models in the area of metacognition – that is, self-planning, prioritization, and process analysis.
This means: AI thinks about its own approach before acting – and that is the crucial difference between automated response and real, systemic intelligence.
🪞 Example
Normal prompt:
“Create a project plan for the relaunch of our website.”
Metaprompt:
“I want a structured project plan for the relaunch of our website.
Please first explain how you would break the task down into logical sub-steps to ensure the plan is complete, realistic, and prioritized.
Then outline the prompt architecture you would use to carry out these sub-steps, and mention any decisions you would make in the UI (e.g., agent mode or tool selection).”
The result: The AI first designs its own solution process – and then delivers the implementation.
A fundamental shift in perspective.
🚀 Significance and outlook
Metaprompting is, in a way, the next evolutionary stage of prompt engineering:
- Reverse prompting → understanding existing prompts
- Prompt engineering → consciously formulating inputs
- Metaprompting → designing prompt systems in which the AI itself selects the best method
This development marks a turning point: Instead of seeing AI as a reactive tool that executes instructions, we are beginning to use it as a cooperative architect of our thinking systems.
💡 Conclusion
Metaprompting is the moment when AI stops being just a tool – and starts to become part of the thinking process itself.
It is the bridge between human intention and machine strategy.
And that’s exactly where true co-intelligence begins.