You already run AI Reviewers on your translations, so you can see when quality slips – a glossary term ignored, a formality rule missed, a score that drops below the bar. Seeing the problem is one thing. Turning it into the right engine change is another: read the failing reviews, spot what they have in common, decide whether the fix is a glossary entry, an instruction, or a brand-voice change, then write it. That second step is the slow part, and it is the one that quietly never gets done.
Engine Suggestions does it for you. When reviews come back low, Lingo.dev reads them, finds the pattern, and proposes the exact edit to your engine's glossary, instructions, or brand voice – with the reasoning attached. You review it and click Apply, or Dismiss it. Low scores in, concrete engine fixes out.
A suggestion is a proposed edit, not a translation change
A suggestion is a pending change to your engine's configuration. Applying one writes a real glossary item, instruction, or brand-voice entry – the same record you would have created by hand. It does not re-translate anything: the change takes effect on the next translation the engine runs.
Automatic suggestions from low scores#
This is the main way suggestions appear. If you run AI Reviewers, every translation is already being scored against your glossary, instructions, and custom criteria. A run of low scores – a failed boolean check, or a percentage under the bar – is a signal that something in the engine needs tuning. Turn on auto-suggestions and Lingo.dev acts on that signal for you: it reads the failing reviews, finds what they have in common, and proposes edits in the background, without you asking.
Enable it from the engine's Reviews tab. From then on, a run of low scores quietly produces suggestions, and you find them waiting in the Suggestions tab – and in a notification, so they do not sit unnoticed.
It batches, it doesn't spam
Auto-generation is debounced to roughly one run per engine every ten minutes, so a burst of low scores produces one considered batch of suggestions rather than a flood. Identical proposals are de-duplicated, so you never see the same edit twice.
Any translation can trigger it#
The signal is the review score, not where the translation came from. Whether the engine ran a synchronous call, an async job, or a job through the full localization pipeline, the result is scored by the same AI Reviewers – and a low score feeds the same suggestions. So the more of your translation traffic runs through reviewed engines, the more the suggestions reflect what is actually going wrong in production.
Generate on demand#
You do not have to wait for the next low score. A Generate suggestions button runs the same analysis immediately – recent low-scoring reviews plus the engine's current config – whether or not auto-suggestions is enabled. Use it after you have made other changes and want a fresh read, or when you would rather pull suggestions than wait for them.
What a suggestion proposes#
Every suggestion targets one of three parts of the engine, and is either an addition or an update to an existing entry:
| Action | What applying it does |
|---|---|
| Add / update glossary item | Creates or changes a glossary rule – an enforced translation, or a term marked non-translatable. |
| Add / update instruction | Creates or changes a per-locale instruction. |
| Add / update brand voice | Creates or changes the brand voice for a locale. |
Each suggestion carries its reasoning – a short explanation of why it is proposing this edit – and the target locale it applies to. You are never asked to trust an opaque change: you read what it wants to do and why before anything is written.
Suggestions are surgical
The model proposes atomic edits – one glossary item, one instruction – not a rewrite of your engine. Each is reviewed and applied on its own, so you can take the three that are right and drop the one that isn't.
Review, apply, dismiss#
Suggestions land in the engine's Suggestions tab as pending. Each one shows the proposed change, the target locale, and the reasoning. Two actions:
Apply
Writes the proposed change into the engine – a real glossary item, instruction, or brand-voice entry. Applying is a deterministic write of the proposed edit: no second AI call, no surprise. The suggestion is marked applied and the change takes effect on the engine's next translation.
Dismiss
Drops the suggestion. Use it when a proposal is wrong for your product – you know your terminology better than any model. Dismissing leaves the engine untouched.
Because applying writes the same kind of record you would create by hand, an applied suggestion is not a black box afterward: it is an ordinary glossary item, instruction, or brand-voice entry you can open, edit, or delete like any other.
Apply does not re-translate
Applying a suggestion changes the engine's configuration, not past translations. Content already translated keeps its existing output until it is translated again. The improvement shows up on the next run through the engine.
Notifications#
When a generation run produces new suggestions, the engine's members are notified – in the app and by email – so improvements do not sit unnoticed in a tab nobody opened. It is the same notification system as the rest of the platform: if you would rather not hear about it, mute Engine suggestions generated in your notification preferences.
Generate from your own feedback#
Not every problem shows up as a low score. Sometimes a linguist or a support ticket tells you, in plain words, exactly what is wrong – "the German copy sounds too stiff", "stop translating the product name". You can feed that text straight in and get the same kind of suggestion, through the Engine Suggestions API. It is the same review-apply-dismiss flow you see here; only the trigger differs – your written feedback instead of a review score.
Reviewers measure, suggestions act#
AI Reviewers and Engine Suggestions are two halves of one quality loop. Reviewers measure – they score each translation against your glossary, instructions, and custom criteria, and tell you where quality is slipping. Suggestions act – they read those low scores and propose the engine change that would raise them. Reviewers find the problem; suggestions draft the fix; you decide. Together they close the loop: translate, score, suggest, apply, and the next translation is better.
