Prompt enhancer

Models already rewrite our prompts. Most systems take what you type, adjust it, then send a stronger instruction to the model. A prompt enhancer brings that process into the open. It sits near the input, suggests concrete additions or rewrites, and turns a rough intent into a clear, constrained prompt before anything runs.

Enhancers support a better user experience and more efficient use of compute power:

  • AI systems feel more approachable by externalizing the “hidden rules” of prompting
  • The user can understand how the AI logically produced its generation, learning to control for bias, improve their own prompting capabilities, and remain in control.
  • A strengthened input inceases the liklihood of getting to a good first result on the first go.

While parameters tune model behavior after the fact, Prompt enhancers improve the instruction itself up front. Tools that expand, clarify, or score prompt completeness move from passive guidance to active assistance, closing skill gaps for non-experts.

Enhancers show up across modalities. In text tools you see “optimize” or “rewrite” controls in editors and playgrounds. In image and video tools you see style chips, prompt expanders, and reference helpers that add structure behind the scenes while keeping the user’s goal intact.

Co-generation

Foundational models and many AI products include a workspace or playground for users to test various prompts against the model. Anthropic includes the option to have AI co-write your prompt with you, taking the user's input and using advanced prompt techniques to include it. In this way, a user can understand how AI processes their prompt into an improved form to logically get the best outcome.

Example of Anthropic transparently showing the user how it would improve their prompt

Design considerations

  • Make enhancement explicit and reviewable. When the system rewrites the prompt, show the new prompt and allow users to continue editing before submitting. In this way, the prompt enhancement feature allows the user to quickly get to a first draft of their intent without abdicating agency over the prompt itself.
  • Replace follow up prompts with actions. Instead of requiring a direct input, systems can use plain-language inline actions like “more concise,” “add examples,” or “cinematic lighting,” then translate them to model-specific syntax under the hood. This reduces cognitive load without hiding capability.
  • Require just enough input before enhancing. If the tool needs a minimum amount of context in order to accurately refine the bar, show a clear character count or relative affordance instead of rendering the action disabled until the arbitary floor is met.
  • Keep user intent and voice intact. Preserve key nouns, constraints, and style cues from the user’s text. Avoid adding facts, opinions, or scope changes unless the user opts in, and highlight any material expansions for easy review.
  • Keep the raw prompt accessible. Let users view, copy, and export the final prompt that will be sent to the model. For power users, expose token count and key parameters so they can manage cost and context.
  • Offer control levels that match expertise. Default to a single “Enhance” action with sensible presets. Provide an “Advanced” drawer for users who want fine-grained knobs like tone, audience, structure, references, or safety filters.

Examples