Free prompt optimiser

Gemini Prompt Optimiser

Structure a Gemini prompt with explicit context, constraints and output format so Google's model has less to guess and less to get wrong.

Try it with this example →

Runs with Local Rules, free, in your browser — no sign-up needed.

What this optimiser checks

  • Whether the destination, dates or traveller details are present
  • Whether the objective states what 'good' looks like
  • Whether constraints such as budget or pace are given
  • Whether the requested output format (list, table, day-by-day) is stated
  • Whether there's a way to verify the plan is realistic

Example prompt

Before
Plan a 3-day itinerary for my trip.

A prompt like "plan a 3-day itinerary for my trip" omits the one thing that actually determines the answer: where, when and for whom. Gemini will produce a plausible-looking itinerary regardless, which is precisely the problem — a confident wrong answer is harder to catch than an obvious gap.

Local Rules scores the prompt across seven categories and rewrites it with the missing specifics called out as placeholders rather than invented, so you fill in the real destination and constraints instead of getting a fabricated one. The output-format section also tells Gemini exactly how you want the result structured, which reduces free-form padding.

Frequently asked questions

Does this use Google's Gemini API automatically?
No. Local Rules runs entirely in your browser with no API call. Gemini is only contacted if you explicitly choose the Gemini direct option and provide your own Google AI Studio API key.
Will the optimised prompt fabricate details like dates or a budget?
No — the rewrite adds labelled placeholders for information you haven't given yet (for example "[destination]"), rather than inventing specifics on your behalf.
Is my API key stored if I use the Gemini direct option?
No. The key lives in memory for that browser tab only, is sent directly to Google's API, and is never written to storage or sent to us.
Optimise your own general prompt →

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