Describe

The best way to understand how to produce high quality or consistent generations, or troubleshoot generations gone awry, is to look under the hood. Describe is a user-invoked action that breaks a generated result into the components that likely produced it, revealing the prompt or a best-guess reconstruction, parameters, and sometimes token or selection details.

Describe is typically trigged by a button or link action. In API-centric tools it shows inputs and logs for a single run. Evidence for this pattern is strongest in image products and developer consoles. Where products do not expose prompts or tokens per run, this action should not appear.

As of late 2025, only a few platforms make use of this as an interface pattern. Notably Midjourney's /describe function reveals the tokens and implied prompt behind an image. This can be applied to images that the generator produced, or human-created files. Using the variations pattern, Midjourney reveals 4 different interpretations of the image, and makes it easy for the user to reproduce each option to evaluate how closely it can reproduce the original tone, subject, and context.

After using token layering and inpainting to generate this abstract image, I tried to reverse engineer it to see the tokens the generator relied on

Alternatively, when interacting directly with an AI, you can simply ask it to share the tokens it relied on. This is not a technique layman users will be familiar with however, so it's unreliable as a solution. Furthermore, unless you are using the open input to communicate with the AI, there is no mechanism to ask for its intent. This leaves the user blind.

The decompose action is closely related to transparent prompts. However, the former is a user-initiated action while the latter supports the visibility of the prompt, tokens, and parameters that went into a generation from shared examples and galleries.

Design considerations

  • Prefer exact extraction over inference. If the artifact contains the original prompt or parameters, show those first. This reduces ambiguity and gives users a faithful starting point. Only infer when exact data is missing.
  • Choose a default detail level, then gate depth. Ship a compact view by default, with an expand for advanced details. This balances legibility for most users against the needs of power users who want logs or probabilities.
  • Limit inferred prompts to a small, distinct set. Return three to four materially different descriptions, not a long list of near-duplicates. This speeds comparison without overwhelming people.
  • Include only parameters that change reproduction in your product. Show the fields that alter outcomes when re-running in your system, such as model or version, seed, guidance, size, or sampler. Omit decorative or non-functional metadata.
  • Make descriptions actionable. Give users an easy path to transform or all of the generated descriptions into new image prompts.

Examples

Midjourney’s /shorten action in discord takes a prompt and breaks it down to its essential pieces and weights to further dive into the aspects of a prompt that are driving the generation.