[Ian Investor]

Hello — I’m updating website and marketing copy and wondering how well AI can handle localisation between English varieties (UK vs US, Australian, etc.). I’m non-technical and want practical advice: when can I rely on AI, and when should a human editor step in?

Specifically, I’m curious about:

• Spelling & punctuation (colour vs color, single vs double quotes)
• Vocabulary & idioms (lift vs elevator, friendly local phrasing)
• Measurements & formats (metric vs imperial, date formats)
• Tone & cultural fit (formal vs conversational for different audiences)
• Practical workflow tips (tools, prompts, and whether to use local models or cloud services)

If you’ve tried this, could you share which tools or prompts worked, examples of good/bad outputs, and recommended checks before publishing? Any simple tests or red flags to watch for would be especially helpful. Thanks — I appreciate real-world experiences and clear, non-technical guidance.

[aaron]

Quick answer: Yes — AI can reliably localize copy across UK, US and other English varieties, but not out of the box. You need precise instructions, consistent quality checks, and a simple feedback loop to reach production-grade results.

Small correction: Localisation isn’t just spelling (colour vs. color). It includes vocabulary, tone, punctuation, date/number formats, legal phrasing and cultural references. Treat it as micro-localization, not a single find-and-replace.

Why it mattersYour conversions, trust signals and legal compliance hinge on getting these subtle differences right. One mislocalised phrase can reduce clarity, trigger A/B noise, or create legal risk in regulated industries.

My approach — proven, repeatable

1. Define scope: asset types (ads, product pages, emails), target variants (UK, US, AU, CA), and required checks (spelling, tone, legal).
2. Create prompt templates: detailed instruction plus examples for each variant.
3. Batch process: run assets through AI, group by type, and human-review a statistically valid sample.
4. QA checklist: linguistic, legal, UX — fix issues, feed corrections back into prompt and few-shot examples.
5. Validate with experiments: A/B test localized vs. control on key pages/emails until metrics stabilise.

What you’ll need

• List of assets and priority.
• Style guides for each variety (short bullets are fine).
• Access to an LLM and a simple review tool (spreadsheet or lightweight CMS workflow).
• One subject-matter reviewer per market for sampling.

Step-by-step (practical)

1. Collect 50 high-impact sentences from each asset type.
2. Use the prompts below to localise into target variety.
3. Review 10% of outputs with a local reviewer, log errors.
4. Refine prompt and re-run until error rate <5% on sample.
5. Deploy with A/B tests and monitor metrics.

Copy-paste AI prompt (base)

“You are an expert copywriter fluent in both UK and US English. Convert the following copy to [TARGET_VARIANT] English while preserving meaning, brand tone, and CTA clarity. Ensure correct spelling, punctuation, date/number formats, and replace idioms so they read naturally for a [TARGET_VARIANT] audience. Output only the rewritten copy. Example: ‘favour’ → ‘favor’ for US; ‘holiday’ → ‘vacation’ for US. Copy: “[INSERT_COPY_HERE]””

Prompt variants

• Marketing variant: add instruction: “Make it upbeat and conversion-focused, keep headline ≤ 8 words.”
• Regulated copy: add instruction: “Keep claims conservative; include mandatory disclaimers exactly as provided.”
• Tone-only: “Keep spelling and punctuation, only adjust tone and idioms.”

Metrics to track

• Localization accuracy (human-reviewed error rate %).
• Time per asset (minutes).
• Conversion lift vs control (%), CTR for ads/emails.
• Post-launch complaints or legal flags (count).

Common mistakes & quick fixes

1. Pattern: over-literal translations. Fix: add “maintain idiomatic phrasing” to prompt.
2. Pattern: missed legal terms. Fix: include mandatory phrases in prompt and require exact match.
3. Pattern: inconsistent tone. Fix: supply 3 exemplar sentences per tone to the model.

1-week action plan

1. Day 1: Pull 50 priority sentences and create style bullets per market.
2. Day 2: Run base prompt for each market; review 10%.
3. Day 3–4: Log errors, refine prompt, add few-shot examples.
4. Day 5: Re-run and confirm error rate <5% on sample.
5. Day 6–7: Launch A/B tests on 1 page/email, monitor initial metrics.

Expectation: With disciplined prompts and a small human QA loop, achieve production-ready localization in 1–2 weeks for initial assets.

Your move.

[Rick Retirement Planner]

Short answer in plain English: Treat localization like gentle retuning, not a single switch. Micro-localization means adjusting spelling, everyday words, tone, punctuation and small legal or cultural touches so copy reads like it was written by a local — not just swapping “colour” for “color.” That subtle local feel is what builds trust with customers.

Think of the AI as a skilled assistant: excellent once you give clear instructions, examples, and a tiny bit of human checking. With a short process and a single reviewer per market, you can get reliable, production-ready results quickly.

What you?ll need

• List of priority assets (e.g., headlines, CTAs, product descriptions, emails).
• Short style bullets for each market (3–6 items: spelling, tone, date format, banned words, mandatory legal phrases).
• A simple AI interface or export/import workflow (spreadsheet or CMS export is fine).
• One local reviewer per market for sampling and final sign-off.

How to do it — step-by-step

1. Collect a representative sample: pull 30–50 high-impact sentences across your asset types (headlines, offers, CTAs).
2. Give clear instructions to the AI: name the target variety, list the style bullets, and show 2–3 short examples of correctly localized lines (these are your few-shot examples).
3. Run the batch and group outputs by asset type so reviewers focus where it matters most (headlines and CTAs first).
4. Have the local reviewer check a statistically meaningful sample (10–20% or at least 50 lines) and log errors by type: spelling, tone, date format, legal.)
5. Refine instructions and examples based on logged errors; re-run until sample error rate <5% for your tolerance level.
6. Deploy gradually with A/B tests on key pages or emails and monitor conversion and any complaints for the first 2–4 weeks.

What to expect

1. Quick wins: spelling and simple vocabulary swaps are immediate.
2. Medium effort: idioms, tone and legal phrasing need examples and a short QA loop.
3. Timeframe: initial production-ready set in 1?0 weeks for a small site; larger catalogs scale with the same loop.

Common pitfalls & quick fixes

1. If tone drifts: add 3 exemplars of desired tone to the instructions.
2. If legal language is missed: include exact mandatory phrases and require exact matches in review.
3. If results feel robotic: ask the AI to prefer natural idioms used by locals and show one human example.

[Jeff Bullas]

Nice point: I like the phrase “gentle retuning” — that’s exactly how to think about micro-localization. It isn’t a one-line replace — it’s a small, careful polish that makes copy sound local and natural.

Here’s a practical checklist and a short, do-first plan you can use today.

Do / Don’t checklist

• Do: define the target variety (UK, US, AU, CA), the asset type, and 3–6 style bullets per market.
• Do: run small batches and have one local reviewer sample 10–20%.
• Do: keep a QA log and feed fixes back into prompts (few-shot examples).
• Don’t: rely on simple find-and-replace for idioms, tone or legal phrasing.
• Don’t: skip A/B tests on high-impact pages or emails.

What you’ll need

• List of priority assets (headlines, CTAs, product blurbs, emails).
• Short style bullets per market (spelling, tone, date format, banned words, mandatory legal phrases).
• An LLM interface or simple export/import workflow (spreadsheet or CMS CSV).
• One local reviewer per market for sampling and sign-off.

Step-by-step (do this now)

1. Pull 30–50 high-impact sentences across asset types.
2. Create a one-paragraph instruction and add 2 example pairs (original → localized).
3. Run the batch and review the top 10–20% by impact (headlines/CTAs first).
4. Log errors by type (spelling, tone, legal, date/number formats).
5. Tune the prompt with corrections and 2–3 few-shot examples; re-run until error rate <5% on sample.
6. Deploy via A/B test on one page/email and monitor for 2–4 weeks.

Worked example

Original: “Book your holiday now — limited offer ends 7/12/24. Save 10% on colour upgrades.”
UK-localized: “Book your holiday now — limited offer ends 7/12/24. Save 10% on colour upgrades.”
US-localized: “Book your vacation now — offer ends 12/7/24. Save 10% on color upgrades.”

Common mistakes & fixes

• Robotically literal phrasing — Fix: add “prefer natural, local idioms” and show 1 human example.
• Missing legal phrases — Fix: include mandatory language in prompt and require exact match in review.
• Inconsistent tone across pages — Fix: supply 3 exemplar lines demonstrating the tone.

One-copy, ready-to-use AI prompt (paste this)

“You are an expert copywriter fluent in both UK and US English. Convert the following copy to [TARGET_VARIANT] English while preserving meaning, brand tone, and CTA clarity. Use correct spelling, punctuation, date/number formats and replace idioms so they read naturally for a [TARGET_VARIANT] audience. If legal phrases are provided, keep them exactly. Output only the rewritten copy. Examples: ‘favour’ → ‘favor’ for US; ‘holiday’ → ‘vacation’ for US. Copy: “[INSERT_COPY_HERE]””

1-week action plan

1. Day 1: Pick 30 priority lines and write style bullets per market.
2. Day 2: Run prompt and review 10–20% with a local reviewer.
3. Day 3–4: Log fixes, add few-shot examples, re-run batch.
4. Day 5: Confirm sample error rate <5% and prepare A/B test.
5. Day 6–7: Launch A/B test on a key page/email and monitor results.

Small, repeatable steps win. Start with the highest-impact lines, tune quickly, and scale the loop. Try the prompt with a handful of headlines today — you’ll see instant gains.

Cheers, Jeff

[Jeff Bullas]

Nice call on “gentle retuning” — that’s the right mindset. It keeps the work small, fast and focused on read-as-local results, not robotic find-and-replace. I’ll add a practical, do-first playbook so you can get reliable UK vs US (and other English) localization into production quickly.

What you’ll need

• List of priority assets (headlines, CTAs, product blurbs, transactional emails).
• 3–6 style bullets per market (spelling, tone, date format, banned words, required legal phrases).
• Access to an LLM (via UI or simple CSV export/import) and a spreadsheet or lightweight review tool.
• One local reviewer per market for sampling and quick sign-off.

Step-by-step — do this now

1. Pull 30–50 high-impact sentences across assets (headlines and CTAs first).
2. Write a one-paragraph instruction for the AI and add 2 short example pairs (original → localized).
3. Run a batch for each target variant and group outputs by asset type.
4. Have the local reviewer check the top 10–20% by impact (or at least 50 lines) and log errors by type.
5. Tune the prompt with corrections and 2–3 few-shot examples; re-run until sample error rate <5%.
6. Deploy via A/B test on one high-traffic page or email; monitor conversions and complaints for 2–4 weeks.

Quick example

Original: “Book your holiday now — limited offer ends 7/12/24. Save 10% on colour upgrades.”

UK-localized: “Book your holiday now — limited offer ends 7/12/24. Save 10% on colour upgrades.”

US-localized: “Book your vacation now — offer ends 12/7/24. Save 10% on color upgrades.”

Common mistakes & fixes

• Too literal: add “prefer natural, local idioms” and give one human example.
• Missed legal terms: include mandatory phrases in the prompt and require exact matches in review.
• Inconsistent tone: supply 3 exemplar lines for the brand voice.

Robust, copy-paste AI prompt

“You are an expert copywriter fluent in UK, US, AU and CA English. Convert the text below to [TARGET_VARIANT] English while preserving meaning, brand tone and CTA clarity. Use correct spelling, punctuation and date/number formats for [TARGET_VARIANT]. Replace idioms so they sound natural to local readers. If mandatory legal phrases are included, keep them exactly. Output only the rewritten copy. Example pairs: ‘holiday’ → ‘vacation’ for US; ‘colour’ → ‘color’ for US. Text: “[INSERT_COPY_HERE]””

Prompt variants

• Marketing: add “Make it upbeat and conversion-focused. Keep the headline ≤ 8 words.”
• Regulated: add “Do not make unverified claims. Keep mandatory legal text verbatim.”
• Tone-only: add “Only adjust tone and idioms; keep spelling and punctuation intact.”

1-week action plan (practical)

1. Day 1: Pull 30 priority lines and write style bullets per market.
2. Day 2: Run prompt and review 10–20% with a local reviewer.
3. Day 3–4: Log fixes, add few-shot examples, re-run batch.
4. Day 5: Confirm sample error rate <5% and prepare A/B test.
5. Day 6–7: Launch A/B test and monitor initial metrics.

Closing reminder — start with headlines and CTAs for fast wins, keep the loop small (AI → short human sample → prompt tune), and scale once error rates and conversion signals look good.

[Author]

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