How AI translation works

If you’ve ever accessed a website in a foreign language, you’ve probably seen this happen. A quick question from your browser: to translate or not to translate? When you say yes, within milliseconds, the website is fully in your default language. You’ve just witnessed AI translation in action.

So, how does that work? This guide breaks down the technology behind AI translation, what's changed with the rise of large language models, what affects output quality, and how modern teams use AI translation safely at scale.

What is AI translation?

AI translation is the use of machine learning models to convert text from one language to another.

Instead of translating word by word, these systems predict the most likely translation based on patterns learned from massive amounts of multilingual text. These can include grammar, syntax, word order, and how meaning shifts across languages.

The quality of the output depends on several factors:

• The language pair (for example, English–Spanish vs. English–Japanese).
• The type of content (UI strings, marketing copy, legal text).
• The amount of context provided.
• Constraints like terminology rules, formatting, and tone.

It’s important to make one clarification here. AI translation doesn’t “understand” language the way humans do. It simply recognizes statistical and semantic patterns and uses those patterns to generate fluent output.

The evolution of machine translation

Machine translation didn’t appear overnight, and in recent years, it has gone through some interesting shifts. Each one has brought meaningful changes and improvements to both fluency and accuracy.

Rule-based machine translation was the earliest approach. Linguists wrote explicit grammar rules and bilingual dictionaries for each language pair. It was precise in narrow domains but brittle, expensive to maintain, and terrible at handling ambiguity or idiomatic language.

Statistical machine translation (SMT) replaced rules with probability. These systems analyzed large parallel corpora and learned statistical relationships between words and phrases. SMT was better in fluency, but it still operated at the phrase level and often produced output that felt choppy across longer passages.

Neural machine translation (NMT) changed the game. It stopped working phrase by phrase and started processing entire sentences, learning more patterns. This brought a more fluent, natural-sounding output and made NMT the backbone of tools like Google Translate and DeepL around 2016-2017.

The last piece in the evolution puzzle is LLM translation. LLMs can reason across longer contexts, adapt tone, and handle ambiguity more naturally. But they also introduce new risks, especially around predictability and control.

How neural machine translation (NMT) works 

Neural machine translation systems follow an encoder-decoder architecture.

The encoder reads the source text and converts it into a numerical representation that captures meaning and structure. The decoder then generates the target language output based on that representation.

There is one detail that makes NMT powerful. Unlike previous models, it can learn sequences and relationships instead of isolated words. That means it can handle grammar, agreement, and word order more effectively, resulting in a more natural-sounding output.

NMT translation is fast, scalable, offers consistent output for similar inputs, and has deterministic behavior when configured correctly. Plus, it works well for high-volume, repeatable content.

On the downside, it struggles with domain shifts without tuning and can mishandle variables, placeholders, and formatting if not controlled.

How LLM translation works (and why it’s different)

LLMs have an entirely different approach to translation. Unlike NMT, they’re not trained only on parallel text. They learn from vast amounts of monolingual and multilingual text across the internet.

From there, they develop a broad understanding of how language works, including grammar, tone, cultural context, and conversational patterns. All this gives them several advantages over NMT.

• They’re better at handling tone and register. If your source material is casual and conversational, an LLM has more chances of preserving that.
• They handle ambiguity more gracefully because they can use broader context to understand words that have multiple meanings.
• They can maintain meaning and style across paragraphs.
• They’re better with idioms and culturally specific expressions, often producing natural equivalents rather than awkward literal translations.

All these advantages do come at a price, and LLM translation isn’t always perfect. These models can produce wrong outputs and are susceptible to style drift as they may subtly shift tone or terminology over a long document.

Sometimes, they can mishandle variables and structured content, inserting or removing placeholders, HTML tags, or formatting tokens. And their output is non-deterministic, meaning the same input can produce slightly different translations each time, which complicates quality assurance.

Why LLMs can be better for customer-facing content

In customer-facing perception matters just as much as accuracy.

When users read a landing page, onboarding flow, or help center article, they’re evaluating your brand. 

If they land on a stiff, unnatural translation, native speakers will instantly know you don’t truly understand their language. That’s something that will 100% erode trust, even if the information is correct.

LLMs often perform better in these situations because they prioritize natural phrasing and contextual coherence. That makes them particularly well-suited for:

• Marketing pages that rely on tone and emotional nuance.
• Onboarding flows where clarity reduces friction.
• Help center content that needs to sound supportive, not robotic.
• Product messaging where brand voice must remain consistent.

What influences AI translation quality

AI translation has better quality than, for instance, NMT, but we know it’s not always perfect. So, what exactly influences its quality?

Context

Short, isolated strings, especially UI labels, are a common source of errors. Without context, models must guess the meaning.

To counter this, try to provide surrounding text, screenshots, and metadata. This usually improves disambiguation and accuracy.

Terminology and brand voice

Every brand has terms it uses consistently: product names, feature labels, industry-specific language. But AI can’t know that unless you explicitly tell it.

Glossaries and protected terms solve this. Style rules that define your brand voice in each target language also help. Specify the formality level, if and when to use formal address, and how to handle terms that should stay the same in all languages.

Content type and risk

A tooltip in your app has a different risk level than a pricing page, a legal disclaimer, or a patient-facing healthcare communication. AI may see the difference, but it can’t understand the different risk profiles, and it won’t change its behavior unless prompted.

Effective AI translation strategies route content differently based on type and risk, applying heavier quality controls to high-stakes content while letting lower-risk content move faster.

Language pair complexity

Some language pairs are just harder than others. English to Spanish, for instance, is relatively easy and common. There are many structural similarities, so most LLMs will do well with them.

English to Japanese, Arabic, or Finnish introduces challenges around morphology, word order, formatting, levels, and idiomatic expressions that increase the chance of errors. 

How AI translation is evaluated 

The one way to understand how well something is working is to evaluate it. With AI translation, there are three main approaches.

The starting point is usually automated metrics like BLEU, METEOR, and COMET. These compare AI output against human reference translations. They’re useful for benchmarking and tracking trends over time, but they have a few blind spots.

• They rely heavily on word overlap. That means they can miss meaning shifts or tone issues that don’t show up as vocabulary differences.
• They work best as directional signals, not as definitive quality scores.

And that’s where human evaluation can make a difference. You can have lightweight spot checks run by bilingual team members or a formal linguistic quality assessment (LQA) using standardized error taxonomies. The key is to build this review into your workflow and apply it to key areas, not to every single word.

The last part in evaluating AI translation is categorizing errors. This is where you can go from purely measuring, from flagging something as a “bad translation” to tracking errors by types and taking action. For instance, you can spot:

• Accuracy issues (meaning altered or lost).
• Fluency problems (grammatically correct but unnatural).
• Terminology inconsistencies (glossary terms not followed).
• Formatting breaks (variables, tags, or links mishandled).

When you can see what kind of errors are happening more often, you can fix the root cause instead of reviewing everything manually.

On their own, these three methods offer a good understanding of how well AI translation is working, but not a complete view. For that, you’ll need a workflow that combines all of them, leaving no blind spots.

How AI translation fits into a modern translation workflow

The LLM engines themselves are only one piece of the puzzle. What brings everything together is your ability to integrate AI translation across teams. A modern workflow includes several steps.

1. Content intake and change detection

The moment there’s a change in the original text, the translated version should be automatically updated as well. Modern systems monitor your website, app, CMS, or codebase and detect new or updated strings in real time.

This prevents content drift across languages and ensures global users aren’t seeing outdated messages. Having automation at this stage means you no longer need manual file exports or tracking spreadsheets.

2. Routing decisions

A product description and a legal disclaimer aren’t the same type of content, so why should they follow the same path when it comes to translation? 

Smart workflows route content based on language pair, content type, and business impact. They select the appropriate engine, review level, and quality controls, which ensures speed where possible and caution where necessary.

3. AI translation pass

Once routed, the content goes through the selected AI engine. At this stage, you should be ready to enforce terminology rules, glossaries, and style constraints to reduce inconsistencies.

The goal is a translation that’s well-structured, has governed output, and is fast. When configured correctly, this step delivers high-volume translation in seconds while maintaining alignment with brand standards.

4. Automated QA checks

Another important step in how AI translation works is the automated quality checks. Before publishing content, these checks validate structure integrity. This includes confirming that variables, placeholders, HTML tags, numbers, and links remain intact.

These checks prevent subtle but costly errors like broken UI elements or corrupted formatting. Automated QA acts as a safety net, catching issues before customers see them.

5. Human review where it matters

Human review is a must-have, but if you apply it to every single line, it will be more of a hassle than help. So, what benefits from it? High-impact content, such as legal language, product launches, or critical communications are all up on the list.

Lower-risk content can move forward without manual intervention. This targeted approach preserves speed and allocates human effort where it can provide the most value.

6. Publish, monitor, iterate

Translation isn’t over when you hit publish. You’ll need to monitor quality signals, track user feedback, and watch for regression when content or models change.

In time, you can refine workflows: adjust routes, update glossaries, or introduce new engines where appropriate. 

This should give you a good picture of why it’s best not to rely on AI engines alone. Instead, use translation management platforms that integrate with your tech stack, enforce terminology, monitor quality, and keep multilingual content automatically in sync. 

Platforms like Localize can help operationalize AI translation across engineering, product, and marketing teams, so that speed doesn’t come at the expense of quality.

Common pitfalls (and how to avoid them)

You might understand everything there is to understand about how AI translation works and still fall into a few common traps.

• Treating AI translation as a one-time project. Websites and products change constantly. If your translation process doesn't automatically detect and handle updates, you'll end up with outdated translations that drift from your source content.
• Not enforcing terminology and brand rules. Without glossaries and style guides, AI models will make inconsistent choices, and your brand consistency will suffer.
• No quality measurement. If you're not tracking translation quality, you won't know when it degrades. Silent regressions, where quality drops with no one noticing, are one of the most common problems in scaled translation programs.
• Over-reviewing everything. Routing all content through human review defeats the purpose of automation. It slows everything down and burns out reviewers. 
• Relying on a single engine forever. Some engines perform well in certain languages but poorly in others, and vice versa. And they all go through changes very quickly. Be flexible in your choices and ready to switch models when needed.

When you should (and shouldn’t) use AI translation

Should you use AI translation? In most cases, the answer will be yes. That being said, there are moments when this will be preferable and cases when it would be best to avoid it.

Great use cases include:

• High-volume web content.
• Fast product iterations.
• Support documentation.
• Multilingual marketing sites.
• Continuous localization pipelines.

Use with caution, or not at all if you’re translating things like:

• Legal or regulatory content.
• Medical or safety-critical information.
• Pricing and contract language (unless tightly controlled and reviewed).

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