The AI won't always get the right response on the first shot. The ability to regenerate a prompt instructs the AI to try again.

In many cases, this generates a new variations, allowing the user to navigate between their options and then choose the best fit. In this way, it can operate as a type of remix input, building off the same tokens but possibly playing with different weights behind the scenes.

An anti-pattern occurs when the user regenerates the prompt but it writes over the first version. In these cases, the user can't take pieces of the prompt that worked and blend it with a new result. The AI has moved their agency (this is the case with Github Copilot's inline prompts).

The first time a user runs a prompt, they see the AI's first best guess of what the most probable output should be that meets the their intent. However, there may be other logic paths the AI might take through the data to get to a similarly good or even better response. Running the prompt multiple times gives users a better sense for how the AI is understanding their prompt, and a better shot to get a good result.

From here, the user can keep regenerating a response, or rework their prompt, add tuners, and constrain the AI to get where you they want it to go.

Details and variations

As a pattern, the ability to regenerate is fairly simple and well adopted. What sets great implementations apart are the details:

  • Combine this feature with the ability to see multiple variants for the user to choose from
  • Keep a history of regenerations through the use of footprints so users can easily retrace their steps
  • Don't just apply this to results. Consider how this pattern can help source better followups, or even re-generate prompt suggestions



See the map

Encourage users to play with regeneration to better understand the AI's logic. When the user is in discovery mode, issues like hallucinations are less likely to be disruptive, and they may uncover new aspects to the space they are exploring that influence their prompting strategy.

Potential risks

Let me go back

The first time you generate a result, there may be things you like and things you don't. Letting a user regenerate the result without keeping the first can be very frustrating, since there is no way to retrieve what you liked about the first and there is no way to compare. Keep the agency in the user's hands, and only overwrite data when they explicitly command it.


Grammarly allows the user to regenerate their response before replacing the original text
Regenerator in action with the Umso site generator
Regenerations can apply to more than just final results. Here, Perplexity lets the user regenerate new followups
Midjourney gives user the option to "rerun" their prompt
Regenerations will often be listed as variations. ChatGPT will often add a rating as well
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