What's The Prompt For That AI Image? (Here's the Trick)

What's The Prompt For That AI Image? (Here's the Trick)

by Matt Wolfe

Applied Sciences & Engineering AI — Models & Research UYQArtificial intelligence (AI)UYQNNeural networks and fuzzy systems
🎙 Matt Wolfe 👥 1.0M 📅 April 4, 2023 ⏱ 13 min 👁 70K 📄 tutorial 🧭 2026-08-28
Available in: English (current) Français

Keywords

reverse engineeringpromptMidjourneyStable DiffusionCLIP Interrogator

Summary

In this tutorial, Matt Wolfe addresses a common question from his audience: how to determine the prompt used to generate a specific AI image. He presents three methods. The first is for Stable Diffusion images: the PNG Info feature, which can extract the exact generation parameters (prompt, seed, sampler, etc.) if the image was saved with that metadata. The second method uses the CLIP Interrogator, a Hugging Face space that analyzes any image and suggests a prompt that could generate a similar image. The third method is Midjourney’s /describe command, which provides four prompt suggestions for an uploaded image. Throughout the video, Wolfe tests each method on various images, comparing the generated results to the originals. He concludes that while none of the methods are perfect, they are useful for learning prompt techniques and styles. He also mentions that the /describe feature is particularly helpful for real photos, as it can suggest prompts to achieve a desired look.

158 words

Critical Evaluation

Value of the Information & Strength of the Argument

The video provides practical, actionable information. It demonstrates each method in real-time, showing the exact steps and the results obtained. The argumentation is straightforward: the presenter explains the strengths and limitations of each method, based on his own tests. He does not overstate the accuracy of the tools, acknowledging that they provide approximations rather than exact replicas. The value lies in the comparative analysis of the three methods, which helps the viewer choose the most appropriate one for their needs. The video also highlights the importance of saving generation parameters in Stable Diffusion, which is a useful tip for consistency in one’s own work.

Scientific Rigor, Source Quality, Title Accuracy

The video is a tutorial, not a scientific study. The information is based on the presenter’s personal experience and testing. The sources cited are the tools themselves: the Hugging Face CLIP Interrogator space, and the Midjourney /describe feature. The presenter also mentions his own website and newsletter, which are not directly related to the topic. The title accurately reflects the content. The video does not provide any external references or citations to support the claims, but the claims are about the functionality of the tools, which is demonstrated. The video is honest about the limitations of the methods, which adds to its credibility.

222 words

Title / Content Match

The title accurately reflects the content: the video demonstrates how to find the prompt for an AI-generated image using three different tools.

Quality & Reliability

7/10

The video provides practical, hands-on demonstrations of three methods to reverse-engineer AI image prompts. The methods are clearly explained and tested with real examples. The information is accurate as of the publication date, but the tools and interfaces may have evolved since then. The video does not delve into the theoretical limitations or potential biases of the methods, but it is honest about the approximate nature of the results.

Key Moments

Markers derived by PSI from the transcript: the creator did not define chapters.

Cited Sources

Concurring Sources

Contribution & Novelties

The video offers a practical, comparative overview of three methods for reverse-engineering AI image prompts, which is a common need for AI art enthusiasts. It highlights the strengths and limitations of each method, providing a useful decision framework. The demonstration of the /describe feature in Midjourney is particularly timely, as it was released the same day. The video also emphasizes the importance of saving generation parameters in Stable Diffusion for reproducibility.

Pour aller plus loin :

  • CLIP (Contrastive Language-Image Pre-training) — The model underlying the CLIP Interrogator, which aligns images and text.
  • Prompt engineering — The practice of crafting prompts to guide AI models, central to the video’s topic.
  • Stable Diffusion — The open-source text-to-image model, one of the tools discussed.
  • Midjourney — The AI art generator that introduced the /describe command.

132 words

Radar Profile

The radar profile shows a balanced performance across all dimensions, with slightly higher scores in information quantity and quality, and a slightly lower score in technical depth. This reflects the video's practical, tutorial nature, which is accessible to a broad audience while still providing useful information.

Reliability 7/10

💬 Très positif. Sur les 30 commentaires analysés, l'écrasante majorité exprime de la gratitude et de l'enthousiasme pour le contenu, avec des remerciements et des éloges pour la clarté et l'utilité des explications.