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tips_and_updatesAn honest answer

Why not just
use AI to translate?

Honestly? For small projects, you should. AI translation is fast, cheap, and increasingly good. We've built it directly into Locize for exactly that reason.

The question is not whether AI can translate your strings. It's whether AI can manage them across a team, over time, without developer involvement for every change.

translate

AI translates strings

Given a string in English, Claude or ChatGPT produces a string in French — in any of 95+ languages, with i18next placeholders intact. Quickly, cheaply, usually well. This is genuinely solved. Use AI for this; we do.

settings

A TMS manages them

Which keys changed since last week? Which languages are incomplete? How do you push a copy fix without a deployment? Who can edit translations without touching the repo? These are system problems. AI doesn't solve them.

Where the DIY workflow breaks down

Six problems that appear when a real product grows past the initial translation sprint.

key

Key lifecycle management

AI onlyTranslates the strings you give it. Has no idea a key was added, changed, or removed.
LocizesaveMissing automatically captures new keys in development. Unused key detection flags stale translations.
psychology

Context and consistency

AI only"Drive" could be a verb, a storage location, or a brand name. Without context, AI guesses.
LocizeGlossary terms and key-level descriptions are injected into every AI prompt. "Drive" always means what you say it means.
rocket_launch

Deploying a translation fix

AI onlyRun the script → commit JSON → open PR → review → merge → deploy. That's the full cycle for a typo fix.
LocizeTranslator edits in the Locize editor → publishes → change is live via CDN in seconds. Zero developer involvement.
group

Non-developer access

AI onlyGiving a translator or product manager access to translations means giving them access to your repo.
LocizeTranslators have their own UI. They work independently without touching your codebase or deployment pipeline.
history

Audit trail and history

AI onlyGit blame is your audit log. Good luck finding when and why a specific translation changed.
LocizeChanges are logged with who made it and when.
account_tree

Staging vs. production

AI onlyYou manage this yourself, usually by having separate JSON files or branches.
LocizeBranches and Versions let you develop translations for an upcoming release in isolation, then merge, exactly like your Git workflow.

Already all-in on AI? The graduation path

You don't have to throw away your LLM pipeline. Graduating means keeping AI as the translator and adding the management layer around it, in four steps.

This is the failure mode that made headlines when Mozilla moved its support content to AI-first localization and its long-standing Japanese volunteer community resigned: the criticism was not that AI translated, but that nothing enforced terminology and style. The fix is governance, not better prompts.

1

Keep your AI, give it context

Bring your own OpenAI, Gemini or Mistral key (or use the built-in service). Locize feeds your glossary and styleguide into every prompt, so terminology stops drifting.

2

Score every AI translation

Quality Estimation rates each AI translation from 0 to 1. Confident results are saved directly; low-confidence ones are routed to human review instead of shipping silently.

3

Review what matters, with a trail

Reviewers work in context on the running app. Accept/decline history and provenance export show who approved what, when, and with which quality score.

4

Ship over the CDN

Approved translations publish to a global CDN without a redeploy. Your CI pipeline stays; the copy-paste step goes.

Add governance to your AI pipelineRead: when AI translations break →

Common questions

Use AI for translation. Use Locize to manage it.
Locize has AI translation built in (BYOK or our own service). You get the speed of AI with the workflow infrastructure your team actually needs.
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