Prompt Library

ChatGPT Prompts for Translation & Localization

30 copy-paste prompts

Thirty hand-tested prompts for accurate translation that keeps tone, handles idioms and slang, localizes for a target market, and passes back-translation QA.

In short: This page contains 30 copy-paste ready prompts, organized into 6 categories with a description and pro tip for each. The first 15 prompts are free instantly — no signup needed. Hand-curated and tested by the AI Academy team.

By Louis Corneloup · Founder, Techpresso

Last updated May 15, 2026·Hand-curated & tested by the AI Academy team

Accurate Translation with Tone Preservation

5 prompts

Faithful Tone-Matched Translation

1/30

<context> Source language: [SOURCE LANGUAGE] Target language: [TARGET LANGUAGE] Content type: [EMAIL / ARTICLE / PRODUCT COPY] Tone to preserve: [FORMAL / CASUAL / WITTY / AUTHORITATIVE] Source text: """ [PASTE SOURCE TEXT] """ </context> <task> Translate the source text into [TARGET LANGUAGE]. 1. Preserve the exact tone, register, and emotional weight of the original. 2. Do not translate literally where it would sound unnatural; prioritize how a native speaker would actually phrase it. 3. Keep sentence rhythm and emphasis (bold, caps, punctuation) consistent with the source. 4. Output only the translation, then a 2-line note flagging any phrase where tone forced a meaning trade-off. </task>

Produces a natural target-language translation that mirrors the original tone and flags any compromises.

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Pro tip: State the tone explicitly even if it seems obvious; ChatGPT defaults to neutral-formal otherwise.

Register Control (Formal vs Informal You)

2/30

<context> Source language: [SOURCE LANGUAGE] Target language: [TARGET LANGUAGE] Formality: [FORMAL (vous/Sie/usted) OR INFORMAL (tu/du/tu)] Relationship: [BRAND-TO-CUSTOMER / PEER-TO-PEER / EXECUTIVE] Source text: """ [PASTE SOURCE TEXT] """ </context> <task> Translate into [TARGET LANGUAGE] using the specified formality level consistently throughout. 1. Apply the correct second-person form and honorifics for [TARGET LANGUAGE]. 2. Adjust verb conjugations, titles, and politeness markers to match the chosen register. 3. Never mix formal and informal address within the same text. 4. Output the translation, then list which register markers you applied. </task>

Delivers a translation with consistent formality and correct honorifics for the target language.

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Pro tip: Languages like Japanese, Korean, and German punish register mistakes harder than English; always specify.

Brand Voice Carryover

3/30

<context> Source language: [SOURCE LANGUAGE] Target language: [TARGET LANGUAGE] Brand voice: [3-5 ADJECTIVES, e.g. bold, friendly, concise] Forbidden words: [LIST] Source text: """ [PASTE SOURCE TEXT] """ </context> <task> Translate into [TARGET LANGUAGE] while carrying the brand voice across the language gap. 1. Recreate the brand personality, not just the literal meaning. 2. Avoid every word in the forbidden list and propose on-brand alternatives. 3. Keep CTAs punchy and idiomatic for the target market. 4. Output the translation, then a short note on any brand-voice choices that differ from a literal rendering. </task>

Translates copy so it still sounds like your brand in the target language.

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Pro tip: Paste 2-3 example sentences of approved brand copy so ChatGPT can pattern-match the voice.

Nuance & Connotation Preservation

4/30

<context> Source language: [SOURCE LANGUAGE] Target language: [TARGET LANGUAGE] Sensitive concepts: [WORDS WITH STRONG CONNOTATION] Source text: """ [PASTE SOURCE TEXT] """ </context> <task> Translate into [TARGET LANGUAGE] with careful attention to connotation. 1. For each sensitive concept, choose the target word whose emotional charge best matches the source. 2. Where the target language has no exact equivalent, pick the closest and explain the gap. 3. Avoid false friends and words with unintended negative associations. 4. Output the translation, then a table of sensitive-concept choices: source word, chosen target word, reason. </task>

Yields a translation that protects subtle connotation and avoids false friends.

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Pro tip: Ask for the connotation table whenever the text is persuasive or emotional, like ads or apologies.

Three-Variant Translation for A/B Review

5/30

<context> Source language: [SOURCE LANGUAGE] Target language: [TARGET LANGUAGE] Use case: [HEADLINE / TAGLINE / CTA] Source text: """ [PASTE SOURCE TEXT] """ </context> <task> Produce three distinct translations into [TARGET LANGUAGE]. 1. Variant A: closest to the literal meaning. 2. Variant B: most natural and idiomatic for a native speaker. 3. Variant C: punchiest and most marketing-forward while staying accurate. 4. For each, give a one-line note on tone and a back-translation into [SOURCE LANGUAGE] so a non-native reviewer can compare. </task>

Generates three translation options with back-translations so reviewers can pick the best fit.

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Pro tip: Use this for high-stakes short copy where one phrasing choice carries real conversion weight.

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Idioms, Slang & Cultural References

5 prompts

Idiom Localization (Not Literal)

6/30

<context> Source language: [SOURCE LANGUAGE] Target language: [TARGET LANGUAGE] Source text (contains idioms): """ [PASTE SOURCE TEXT] """ </context> <task> Translate into [TARGET LANGUAGE], handling idioms intelligently. 1. Detect every idiom, proverb, or figurative expression in the source. 2. Replace each with an equivalent idiom that exists naturally in [TARGET LANGUAGE], not a literal rendering. 3. If no equivalent exists, translate the meaning plainly and note it. 4. Output the translation, then a table: source idiom, target equivalent (or plain meaning), confidence. </task>

Replaces source idioms with natural target-language equivalents instead of literal nonsense.

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Pro tip: Review the confidence column closely; idiom matches are where machine translation most often fails.

Slang & Internet Register Translation

7/30

<context> Source language: [SOURCE LANGUAGE] Target language: [TARGET LANGUAGE] Audience: [GEN Z / GAMERS / PROFESSIONALS] Platform: [TIKTOK / TWITTER / FORUM] Source text: """ [PASTE SOURCE TEXT] """ </context> <task> Translate slang and informal language into [TARGET LANGUAGE] for the specified audience. 1. Map source slang to current, age-appropriate slang in the target culture, not dictionary equivalents. 2. Keep the casual energy, abbreviations, and platform conventions intact. 3. Avoid slang that is dated, regional-niche, or could read as cringe to a native of the audience. 4. Output the translation, then flag any slang term you were less than confident about. </task>

Translates slang into current, audience-appropriate target-language equivalents.

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Pro tip: Slang dates fast; ask ChatGPT to flag low-confidence terms and verify those with a native speaker.

Cultural Reference Adaptation

8/30

<context> Source language: [SOURCE LANGUAGE] Target language: [TARGET LANGUAGE] Target market: [COUNTRY / REGION] Source text (contains cultural references): """ [PASTE SOURCE TEXT] """ </context> <task> Translate and adapt cultural references for [TARGET MARKET]. 1. Identify references that won't land abroad: celebrities, holidays, sports, pop culture, food, units. 2. Substitute each with an equivalent that resonates in the target market while keeping the original intent. 3. Convert units, currencies, and date formats to local conventions. 4. Output the adapted translation, then a table of every reference swap and why. </task>

Adapts cultural references and local conventions so the text feels native to the target market.

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Pro tip: Keep brand-defining references (your own product names) intact; only swap incidental cultural mentions.

Humor & Wordplay Recreation

9/30

<context> Source language: [SOURCE LANGUAGE] Target language: [TARGET LANGUAGE] Source text (contains jokes/puns): """ [PASTE SOURCE TEXT] """ </context> <task> Translate the humor into [TARGET LANGUAGE]. 1. For each pun or joke, recreate the comedic effect rather than translating the literal words. 2. If the wordplay relies on a sound or spelling that doesn't carry over, invent a new joke with the same spirit. 3. Preserve setup-punchline timing and rhythm. 4. Output the translation, then explain each humor choice and rate how well the joke survived (1-5). </task>

Recreates jokes and wordplay so they still land in the target language.

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Pro tip: Wordplay rarely survives 1:1; accept a reinvented joke with the same spirit over a literal flop.

Offensiveness & Taboo Check

10/30

<context> Source language: [SOURCE LANGUAGE] Target language: [TARGET LANGUAGE] Target market: [COUNTRY / REGION] Source text: """ [PASTE SOURCE TEXT] """ </context> <task> Translate into [TARGET LANGUAGE] and screen for cultural sensitivity. 1. Translate the text naturally for [TARGET MARKET]. 2. Flag any word, gesture, color, number, image, or phrase that could be taboo, offensive, or unlucky in the target culture. 3. Propose safer alternatives for each flag without losing the message. 4. Output the translation, then a risk table: item, why it's risky, suggested replacement. </task>

Translates while flagging culturally taboo or offensive elements with safer replacements.

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Pro tip: Run this before any campaign launch in an unfamiliar market; it catches color and number taboos easily missed.

Localization for a Target Market

5 prompts

Full Market Localization Brief

11/30

<context> Source language: [SOURCE LANGUAGE] Target language: [TARGET LANGUAGE] Target market: [COUNTRY / REGION] Product: [WHAT YOU SELL] Source text: """ [PASTE SOURCE TEXT] """ </context> <task> Localize (not just translate) the text for [TARGET MARKET]. 1. Translate naturally, then adapt examples, tone, and value propositions to local buying behavior. 2. Localize prices, currency, payment methods, units, dates, addresses, and phone formats. 3. Adjust calls to action to match local urgency and politeness norms. 4. Output the localized version, then a checklist of every localization change you made. </task>

Produces market-ready localized copy with local formats, examples, and CTAs adapted.

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Pro tip: Localization is more than language; always name the country, not just the language, since markets differ.

Regional Variant Selection

12/30

<context> Source language: [SOURCE LANGUAGE] Target language: [TARGET LANGUAGE] Specific variant: [e.g. LATIN AMERICAN SPANISH / EUROPEAN PORTUGUESE / SIMPLIFIED CHINESE] Source text: """ [PASTE SOURCE TEXT] """ </context> <task> Translate into the specific regional variant of [TARGET LANGUAGE]. 1. Use vocabulary, spelling, and grammar unique to the named variant. 2. Avoid words that are neutral elsewhere but odd or offensive in this region. 3. Match the variant's preferred punctuation, quotation marks, and number formatting. 4. Output the translation, then list 5 word choices that distinguish this variant from its alternatives. </task>

Translates into a precise regional variant with region-specific vocabulary and formatting.

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Pro tip: Never order just "Spanish" or "Portuguese"; the wrong variant alienates the very market you target.

Landing Page Localization

13/30

<context> Source language: [SOURCE LANGUAGE] Target language: [TARGET LANGUAGE] Target market: [COUNTRY / REGION] Page sections: [HERO, FEATURES, PRICING, FAQ, CTA] Source copy: """ [PASTE PAGE COPY] """ </context> <task> Localize the full landing page for [TARGET MARKET]. 1. Localize each section while keeping the structure and labels intact. 2. Tighten or expand text to respect that the target language may run longer or shorter than the source. 3. Localize SEO-relevant phrases to terms locals actually search for. 4. Output section by section, then flag any line likely to break layout due to length. </task>

Localizes an entire landing page section by section and flags length-driven layout risks.

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Pro tip: German and Finnish often run 30 percent longer than English; the length flags save you a design round.

SEO Keyword Localization

14/30

<context> Source language: [SOURCE LANGUAGE] Target language: [TARGET LANGUAGE] Target market: [COUNTRY / REGION] Seed keywords: [LIST OF SOURCE KEYWORDS] </context> <task> Localize keywords for search in [TARGET MARKET]. 1. For each seed keyword, give the term locals actually type, not a literal translation. 2. Add 3-5 local long-tail variants per keyword. 3. Note any keyword where local search intent differs from the source market. 4. Output a table: source keyword, primary local keyword, long-tail variants, intent notes. </task>

Maps source keywords to terms locals actually search, with long-tail variants.

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Pro tip: Literal keyword translations rarely match real search volume; treat these as hypotheses to validate in a keyword tool.

UI String & Microcopy Localization

15/30

<context> Source language: [SOURCE LANGUAGE] Target language: [TARGET LANGUAGE] Context per string: [BUTTON / ERROR / TOOLTIP / EMPTY STATE] Max length per string: [CHARACTER LIMIT IF ANY] Strings: """ [PASTE KEY: STRING PAIRS] """ </context> <task> Localize each UI string for [TARGET LANGUAGE]. 1. Translate each string for its UI context, not in isolation. 2. Respect character limits; if a string can't fit, propose a shorter on-meaning alternative. 3. Keep placeholders, variables ({name}, %s), and formatting tokens untouched. 4. Output as key: translated_string pairs, then flag any string that exceeds the limit. </task>

Localizes UI microcopy in context while preserving variables and respecting length limits.

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Pro tip: Always pass the UI context per string; "Open" as a button differs from "Open" as a status.

Document & Long-Form Translation

5 prompts

Structured Document Translation

16/30

<context> Source language: [SOURCE LANGUAGE] Target language: [TARGET LANGUAGE] Document type: [CONTRACT / REPORT / MANUAL / WHITEPAPER] Document: """ [PASTE DOCUMENT OR SECTION] """ </context> <task> Translate the document into [TARGET LANGUAGE]. 1. Preserve all structure: headings, numbering, lists, tables, and formatting markers. 2. Keep terminology consistent across the whole document; reuse the same target term for each source term. 3. Do not summarize, omit, or add content; translate faithfully. 4. Output the translated document, then list any term you standardized and the version you chose. </task>

Translates a structured document faithfully while preserving formatting and term consistency.

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Pro tip: For long docs, translate section by section and reuse the standardized-term list to avoid drift.

Legal & Contract Translation

17/30

<context> Source language: [SOURCE LANGUAGE] Target language: [TARGET LANGUAGE] Jurisdiction context: [COUNTRY / LEGAL SYSTEM] Clause(s): """ [PASTE CLAUSE] """ </context> <task> Translate the legal text into [TARGET LANGUAGE] with maximum precision. 1. Use established legal terminology in the target language; do not paraphrase legal terms. 2. Preserve clause numbering, defined terms (capitalized), and cross-references exactly. 3. Flag any concept that has no direct legal equivalent in the target jurisdiction. 4. Output the translation, then a note: this is a draft and must be reviewed by a qualified legal translator. </task>

Produces a precise legal-text draft that preserves defined terms and flags untranslatable concepts.

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Pro tip: Treat output as a first draft only; legal translation requires a certified human reviewer before use.

Technical Manual Translation

18/30

<context> Source language: [SOURCE LANGUAGE] Target language: [TARGET LANGUAGE] Domain: [SOFTWARE / HARDWARE / MEDICAL / ENGINEERING] Content: """ [PASTE MANUAL SECTION] """ </context> <task> Translate the technical content into [TARGET LANGUAGE]. 1. Use industry-standard target-language terminology for the domain. 2. Keep product names, model numbers, code, and commands in their original form. 3. Preserve warnings, safety notices, and step numbering exactly. 4. Output the translation, then a glossary of every technical term and its chosen target equivalent. </task>

Translates technical manuals with standard domain terminology and an auto-generated glossary.

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Pro tip: Reuse the generated glossary as a reference doc so future sections stay terminologically consistent.

Academic & Research Translation

19/30

<context> Source language: [SOURCE LANGUAGE] Target language: [TARGET LANGUAGE] Field: [DISCIPLINE] Text: """ [PASTE ABSTRACT OR SECTION] """ </context> <task> Translate the academic text into [TARGET LANGUAGE]. 1. Maintain formal academic register and discipline-specific conventions. 2. Keep citations, author names, and reference formatting unchanged. 3. Translate specialized terms with their accepted target-language equivalents; transliterate where standard. 4. Output the translation, then flag any term where multiple accepted translations exist. </task>

Translates academic writing in a formal register with citations and terminology intact.

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Pro tip: Check journal or institutional style guides; some require specific term translations or English-kept terms.

Email & Correspondence Translation

20/30

<context> Source language: [SOURCE LANGUAGE] Target language: [TARGET LANGUAGE] Relationship: [CLIENT / PARTNER / COLLEAGUE] Desired impression: [WARM / PROFESSIONAL / APOLOGETIC] Email: """ [PASTE EMAIL] """ </context> <task> Translate the email into [TARGET LANGUAGE]. 1. Use the greeting, sign-off, and politeness conventions normal for business email in the target culture. 2. Match the desired impression while preserving every concrete detail (dates, amounts, names). 3. Adapt directness to local norms; soften or sharpen as the culture expects. 4. Output the translated email, then note any cultural adjustment you made to tone or structure. </task>

Translates business email with culturally appropriate greetings, sign-offs, and directness.

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Pro tip: Email norms vary widely; Japanese business email expects far more preamble than a US equivalent.

Subtitles, Captions & Media

5 prompts

Subtitle Translation with Timing

21/30

<context> Source language: [SOURCE LANGUAGE] Target language: [TARGET LANGUAGE] Reading-speed limit: [CHARS PER SECOND, e.g. 17] Subtitles (SRT or timecoded): """ [PASTE SUBTITLE BLOCK] """ </context> <task> Translate the subtitles into [TARGET LANGUAGE]. 1. Keep every timecode and cue number exactly as in the source. 2. Translate so each cue stays readable within the timing and reading-speed limit; condense without losing meaning. 3. Break lines naturally and keep each line within typical caption width (about 42 chars). 4. Output valid SRT, then flag any cue too long to read comfortably. </task>

Translates subtitles into valid SRT that respects timing and reading-speed limits.

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Pro tip: Condensing is essential; if a cue overflows, cut filler words before cutting meaning.

Dubbing-Ready Script Translation

22/30

<context> Source language: [SOURCE LANGUAGE] Target language: [TARGET LANGUAGE] Sync needs: [LIP-SYNC / VOICEOVER] Script: """ [PASTE SCRIPT WITH SPEAKERS] """ </context> <task> Translate the script for dubbing into [TARGET LANGUAGE]. 1. Match the syllable count and rhythm of each line as closely as possible for sync. 2. Preserve speaker labels and stage directions. 3. Keep the emotional delivery and character voice distinct per speaker. 4. Output the translated script line by line, then flag lines where length will make sync hard. </task>

Translates a script for dubbing with length matching and preserved character voice.

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Pro tip: For lip-sync, prioritize syllable count and open-mouth vowels over a perfectly literal line.

Caption & Accessibility Localization

23/30

<context> Source language: [SOURCE LANGUAGE] Target language: [TARGET LANGUAGE] Content type: [VIDEO / PODCAST / WEBINAR] Transcript with sound cues: """ [PASTE TRANSCRIPT] """ </context> <task> Localize closed captions into [TARGET LANGUAGE]. 1. Translate spoken dialogue naturally and concisely for on-screen reading. 2. Localize non-speech cues ([applause], [music], [laughs]) into target-language conventions. 3. Identify speakers consistently and keep their labels. 4. Output the localized captions, then note any sound cue with no standard target-language form. </task>

Localizes closed captions including non-speech sound cues and speaker labels.

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Pro tip: Keep sound-cue brackets in the target language style; do not leave English [music] cues in localized captions.

Social Video Caption Localization

24/30

<context> Source language: [SOURCE LANGUAGE] Target language: [TARGET LANGUAGE] Platform: [TIKTOK / REELS / SHORTS] Caption + on-screen text: """ [PASTE TEXT] """ </context> <task> Localize the social video text into [TARGET LANGUAGE]. 1. Translate hooks and on-screen text to hit fast, scroll-stopping in the target language. 2. Localize hashtags to ones the target audience actually follows. 3. Keep on-screen text short enough to read in the same beat as the source. 4. Output the caption, on-screen text, and localized hashtags separately. </task>

Localizes social video captions, on-screen text, and hashtags for a target platform.

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Pro tip: Translated hashtags are usually wrong; ask for hashtags locals actually use, not literal renderings.

Voiceover Pronunciation Guide

25/30

<context> Source language: [SOURCE LANGUAGE] Target language: [TARGET LANGUAGE] Script with names/brands: """ [PASTE SCRIPT] """ </context> <task> Translate the voiceover script and add a pronunciation guide. 1. Translate the script naturally for spoken delivery in [TARGET LANGUAGE]. 2. For every brand name, proper noun, and foreign term, add a phonetic respelling for a native target-language reader. 3. Mark words that should keep source pronunciation versus be localized. 4. Output the script, then a pronunciation table: term, respelling, keep or localize. </task>

Translates a voiceover script and adds a phonetic pronunciation guide for names and brands.

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Pro tip: The pronunciation table saves recording-session time and prevents your brand name being mangled.

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Glossary Consistency & Back-Translation QA

5 prompts

Glossary-Locked Translation

26/30

<context> Source language: [SOURCE LANGUAGE] Target language: [TARGET LANGUAGE] Glossary (must use exactly): """ [SOURCE TERM => TARGET TERM, one per line] """ Source text: """ [PASTE SOURCE TEXT] """ </context> <task> Translate into [TARGET LANGUAGE] enforcing the glossary. 1. Use the exact target term from the glossary every time its source term appears. 2. Never substitute a synonym for a glossary term. 3. Flag any source term that is in the glossary but ambiguous in context. 4. Output the translation, then a compliance report: glossary term, times used, any deviations and why. </task>

Translates with strict glossary enforcement and a compliance report.

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Pro tip: Maintain one master glossary across all content so terminology never drifts between documents.

Terminology Extraction for a Glossary

27/30

<context> Source language: [SOURCE LANGUAGE] Target language: [TARGET LANGUAGE] Domain: [INDUSTRY / PRODUCT AREA] Source corpus: """ [PASTE REPRESENTATIVE TEXT] """ </context> <task> Build a translation glossary from the source corpus. 1. Extract every recurring key term, product name, and domain phrase. 2. Propose the preferred [TARGET LANGUAGE] equivalent for each, with one alternative. 3. Mark terms that must stay untranslated (brand names, code). 4. Output a table: source term, preferred target term, alternative, translate or keep, notes. </task>

Extracts key terms from a corpus and proposes a structured bilingual glossary.

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Pro tip: Run this once at the start of a project so every later translation locks to the same terms.

Back-Translation QA

28/30

<context> Original source language: [SOURCE LANGUAGE] Translated into: [TARGET LANGUAGE] Original source text: """ [PASTE ORIGINAL] """ Translation to check: """ [PASTE TRANSLATION] """ </context> <task> QA the translation via back-translation. 1. Translate the [TARGET LANGUAGE] version back into [SOURCE LANGUAGE] literally. 2. Compare the back-translation to the original and list every meaning difference, omission, or addition. 3. Rate each difference: critical, moderate, or stylistic. 4. Output the back-translation, the difference list, and a fixed version of the translation. </task>

Checks a translation by back-translating it and listing every meaning difference, then fixes it.

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Pro tip: Run back-translation QA on anything legal, medical, or safety-related before it ships.

Bilingual Accuracy Audit

29/30

<context> Source language: [SOURCE LANGUAGE] Target language: [TARGET LANGUAGE] Source: """ [PASTE SOURCE] """ Translation: """ [PASTE TRANSLATION] """ </context> <task> Audit the translation against the source. 1. Go segment by segment and score accuracy, fluency, terminology, and tone (1-5 each). 2. Flag mistranslations, untranslated chunks, number or name errors, and register slips. 3. Suggest a corrected version for every flagged segment. 4. Output a scored table by segment, then an overall quality verdict and top 3 fixes. </task>

Audits a translation segment by segment with accuracy scores and prioritized fixes.

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Pro tip: Use the scored table to decide whether the translation needs light edits or a full redo.

Consistency Sweep Across Files

30/30

<context> Source language: [SOURCE LANGUAGE] Target language: [TARGET LANGUAGE] Glossary (if any): """ [SOURCE => TARGET PAIRS] """ Translated segments from multiple files: """ [PASTE SEGMENTS, labeled by file] """ </context> <task> Sweep the translated segments for consistency. 1. Find every case where the same source term is translated differently across files. 2. Recommend one canonical target term per source term and list where to fix. 3. Catch inconsistent formatting, capitalization, and tone between files. 4. Output a discrepancy report: term or issue, variants found, recommended fix, files affected. </task>

Finds and reconciles inconsistent term translations and formatting across multiple files.

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Pro tip: Run this before final delivery; cross-file term drift is the most common reason localized products feel off.

Frequently Asked Questions

Tell it explicitly to prioritize how a native speaker would phrase the idea over a word-for-word rendering, and name the tone and register you want. The idiom and cultural-reference prompts here force ChatGPT to find natural equivalents instead of literal ones. Always specify the target market, not just the language.

For drafts, internal content, and high-volume work it is fast and surprisingly good, especially between major languages. For legal, medical, or safety-critical text, treat the output as a first draft and have a qualified human translator review it. The back-translation QA prompt is the best way to catch meaning errors before anything ships.

Translation converts words from one language to another; localization adapts the entire message, including examples, currency, units, dates, idioms, and cultural references, so it feels native to a specific market. The localization category here handles that fuller adaptation. Always name the country or region, since the same language can differ significantly across markets.

Build a glossary once with the terminology-extraction prompt, then use the glossary-locked translation prompt for every piece so ChatGPT reuses the exact same target term each time. Run the consistency-sweep prompt before final delivery to catch any drift across files. A shared master glossary is what separates polished localization from a patchwork.

Yes. Replace every [SOURCE LANGUAGE], [TARGET LANGUAGE], [TARGET MARKET], and similar bracket with your real values before sending. The more specific you are about tone, audience, region, and format, the better the translation. Leaving placeholders in produces vague or generic output.

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