Experiment

Prompt Mirror

Paste a rough prompt and apply rules to make it clearer - add a role, strip hedging, set an output format. No AI, just structure (the way your instruction is organized so an AI understands it better).

Your input stays in the browser — nothing is sent to a server. Privacy policy.

Best for

  • Anyone writing prompts for LLM workflows.
  • Teams creating reusable prompt templates.
  • Writers turning vague asks into concrete instructions.

When to use

  • A prompt feels fuzzy and output quality is inconsistent.
  • You need a clearer role, task, and format section.
  • You want to tighten wording before sending to a model.

Rules

Choose improvements

Each rule rewrites one aspect of your prompt. Toggle them on or off and combine freely.

Hover a rule to see what it does. Rules apply in order.

Input

Your prompt

Paste your rough prompt — the vaguer, the more the rules help.

Output

Improved prompt

Improved prompt appears here as you type.

Before

Give me ideas for making onboarding better.

After

Role: SaaS onboarding strategist. Task: suggest 8 onboarding improvements. Format: table with idea, user friction, expected impact, and first test.

Experiment scope

What it does

Applies local structure rules to a rough prompt: adds a role if missing, strips hedging phrases, sets output format, and flags unclear task boundaries.

What it does not do

Call an AI model, validate whether the improved prompt will produce better output, or adapt to your domain context.

Why it is still an experiment

The ruleset is deliberately narrow. How well fixed structure rules map to real prompt quality varies by use case, and the rule categories are still being refined.

FAQ

Does Prompt Mirror call an AI model?

No. It applies local structure rules in your browser and does not send the prompt to an AI service.

When should I add a role to a prompt?

Add a role when it clarifies the perspective, audience, or quality bar you want the model to use.

Can I use this for reusable prompt templates?

Yes. It is useful for turning rough asks into clearer role, task, format, and constraint sections.

Found an issue or have a suggestion? Report an issue or suggest an improvement.

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