Is this just machine translation?

No. Translation swaps words; localization adapts assumptions. The adapter prompt rewrites currencies, units, date formats, idioms, holidays, names, and tone so the prompt feels native to the target culture rather than translated.

What does the cultural bias scan detect?

It flags US-centric defaults like dollar amounts, imperial units, MM/DD dates, "ZIP code", US-only holidays, and informal address such as "guys". These often slip through unnoticed and break a prompt in other markets.

Do I need an API key?

No. This tool builds an adapter prompt locally in your browser and runs the bias scan with no network calls. You then paste the prompt into whatever LLM you already use.

How many locales are supported?

Thirty-plus, spanning major European, Asian, Middle Eastern, and Latin American markets, each with its own currency, date, unit, and formality conventions baked into the adapter instructions.

Will it keep my prompt's intent?

Yes. The generated instructions explicitly tell the model to preserve the original purpose and only change locale-specific surface details, then explain each change so you can verify nothing important shifted.

What is the Prompt Localization Adapter?

Rewrites a prompt's assumptions, examples, idioms, currencies, units, and dates to be culturally appropriate for a target locale, and flags implicit cultural biases in the original before you ship it worldwide. It runs free in your browser on Gera Tools, with nothing uploaded.

Prompt Localization Adapter

Name: Prompt Localization Adapter
Creator: Gera Tools
License: https://creativecommons.org/licenses/by/4.0/

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Prompt localization adapter

A prompt that works perfectly for one market often breaks quietly in another. It quotes prices in dollars, uses miles and Fahrenheit, assumes a Monday-to-Friday US work week, or addresses everyone as “guys”. The prompt localization adapter takes your original prompt, scans it for these implicit cultural assumptions, and generates a ready-to-run adapter prompt that rewrites it for a target locale — preserving intent while making every surface detail feel native.

Localization versus translation

Most teams translate prompts — they pass the text through a language model and call it done. Localization is different. A translated Japanese prompt might use the right words but still quote prices in dollars, reference a Thanksgiving example, or use an informal address style that would strike a Japanese reader as abrupt. The prompt works linguistically but fails culturally.

The kinds of assumptions that localization must address go well beyond language:

Category	US default	What other locales expect
Currency	$ USD	Local currency and realistic local price points
Dates	MM/DD/YYYY	DD/MM/YYYY (UK, EU) or YYYY-MM-DD (ISO, many Asian markets)
Units	Miles, Fahrenheit, pounds	Kilometres, Celsius, kilograms for most of the world
Address	”ZIP code"	"Postcode” (UK), “PLZ” (Germany), “código postal” (Spain)
Formality	Casual (“Hey guys”)	Formal registers expected in many European and Asian contexts
Holidays	Thanksgiving, Labor Day	Local public holidays and cultural calendars

How it works

You paste your prompt and choose a source and target locale from a list of 30+ markets, each with its own currency, date format, measurement system, and formality norms. The tool runs a quick local scan for US-centric or culture-specific patterns — dollar signs, imperial units, US holidays, “ZIP code”, informal address — and flags each one. It then assembles an adapter prompt that instructs an LLM to localize the original and to append a list of every change it made and every bias it removed. Everything runs in your browser; no key or network call is needed to build the adapter prompt.

Tips and notes

Read the bias flags first. They often reveal an assumption you didn’t know you’d baked in — fix obvious ones by hand before localizing.
Localize tone, not just words. German and Japanese prompts may need a more formal register; Dutch tolerates directness that reads as rude elsewhere.
Watch currency and units in examples. A “$19.99” sample value should become the local currency and a realistic local price, not a direct conversion.
Verify the change list. The adapter prompt asks the model to explain every edit — skim it to confirm nothing load-bearing shifted.
Repeat per market, not once. A single pass to “make it global” rarely works; individual locale adaptations that address the specific audience produce far better results.