J’ai créé le Prompt parfait pour TOUTES les IA (ChatGPT, Gemini, Claude…)

In the rapidly evolving landscape of artificial intelligence, users often find themselves overwhelmed by the sheer variety of available tools, ranging from generative text models like ChatGPT, Claude, and Gemini to specialized applications for coding and image generation like Sora or Nano Banana. However, a significant barrier remains: the quality of the output is entirely dependent on the quality of the prompt. Many users make the mistake of using the same prompting style across different platforms, failing to realize that each model is trained differently and requires specific techniques to yield optimal results. Since becoming a master of "prompt engineering" for every single tool is nearly impossible for the average person, a more automated, universal solution is required. The core solution presented is "Promptor 2.0," a second-generation system designed to be both evolvable and tool-agnostic. Unlike its predecessor, which was limited to ChatGPT, this new iteration is built to adapt to any AI model, including future releases like GPT-5 or Claude 4. It operates on a philosophy of "reverse prompt engineering," where the system starts with the user's specific objective and the chosen AI tool to work backward toward the perfect prompt. ### The Mechanics of the Promptor Framework Promptor 2.0 functions by first assuming a specific persona: an expert in prompt engineering and generative AI agents. The process is structured into a rigorous four-part response system designed to eliminate "hallucinations" and ensure accuracy by forcing the AI to research the most recent benchmarks and user feedback for the specific tool in question. The first phase is **Calibration**. Here, the system identifies three key characteristics that define a perfect prompt for the selected tool. For instance, if a user wants to generate code in Google Gemini, Promptor will highlight that the model prefers concrete deliverables and precision over technical jargon. This phase ensures the final prompt is tailored to the specific "logic" of the target AI. The second phase is the **Drafting** of the initial prompt. Based on the calibration, the system produces a high-quality version of the request. However, the process does not stop there. The third phase involves **Self-Criticism**, where the AI performs a harsh evaluation of its own work, assigning it a star rating out of five. It identifies exactly why the prompt is not yet perfect—perhaps it lacks specific output formats or contextual nuances. The final phase is the **Interrogation**. The system asks the user targeted questions to fill the gaps identified during the self-criticism. This iterative loop continues until a "5-star" prompt is achieved. This method serves a dual purpose: it produces a high-performing prompt and acts as a learning tool for the user, who begins to understand the underlying requirements of effective AI communication. ### Practical Applications and Results The effectiveness of this system is demonstrated through several diverse use cases. In one instance, a user requested a prompt to build a Pomodoro productivity application using Gemini Canva. Promptor identified that Gemini requires it to act as a product owner rather than just a coder. Through the interrogation phase, it refined requirements regarding file formats and interface features, eventually resulting in a fully functional web application named "ZenFocus" that included timers, pause buttons, and daily objectives. In another example, the system was used to generate a professional report on the impact of blue light on sleep using Claude 3.5 Sonnet. By following the Promptor framework, the user obtained a prompt so detailed that the resulting report was comprehensive and ready for immediate distribution. Finally, for image generation using "Nano Banana 2," Promptor recognized that the tool performs best with English-language prompts and specific structural markers. The resulting image—a hyper-realistic smartphone-style photo of the Eiffel Tower during a lightning storm—met all the user's aesthetic and technical criteria. ### Advanced Automation and Knowledge Integration For power users, the transcript outlines how to integrate Promptor into custom environments like ChatGPT’s "GPTs," Gemini’s "Gems," or Claude’s "Projects." A key insight shared is the use of a "Knowledge Base." By performing "Deep Research" on multiple AI models and compiling their best practices into PDF documents, users can feed this data into their custom GPT. This allows the AI to stay updated on the latest prompting techniques without manual intervention. Furthermore, the creator demonstrates the possibility of building a standalone Promptor application using no-code tools like "Google Anti-Gravity." By connecting a Claude API key to a custom interface, users can create a dedicated workspace that is more visual and functional than a standard chat interface. This application allows for rapid drafting, copying, and testing of prompts in a streamlined environment. ### Conclusion and Community The transition from manual prompt writing to using an automated framework like Promptor 2.0 represents a shift toward efficiency and professional-grade AI utilization. By leveraging reverse prompt engineering and iterative self-criticism, users can bypass the steep learning curve of individual AI models. The video concludes by highlighting the "QG de l'IA" (AI Headquarters), a private community where these tools, training modules for N8N and automation, and specialized knowledge bases are shared. The ultimate goal is to provide users with the keys to exploit the opportunities generated by AI, regardless of the specific platform they choose to use.