The fine-tuning trap in AI translation

Why generic fine-tuning contaminates context

Fine-tuning is often sold as “teach the model your brand voice.” But generic fine-tuning doesn’t learn your style in a clean, controllable way. It learns a blended average of whatever you feed it across time, teams, and content types. That’s why it leads to contextual contamination.

What “contextual contamination” actually means

Contextual contamination happens when a fine-tuned model starts making confident translation choices based on the wrong internalized context. It can be language that belongs to a different product area, a different team, or a different period in your release history. In enterprise translation programs, training data rarely comes from one clean, stable source. It’s typically a mix of:

• Time contamination (old releases): Legacy feature names, deprecated UI strings, past tone decisions, or last quarter’s terminology that still exists in the dataset. The model can’t “know” what’s current.
• Domain contamination (mixed content types): Marketing copy, UI strings, help docs, legal, release notes, and support tickets often end up in the same training set. The risk of style bleed is real: your UI might start sounding like marketing, or your help center like a product tooltip, which is not ideal.
• Decision contamination (human edits as “truth”): Reviewer edits aren’t always consistent or intentional. Some are preferences, some are workarounds, some reflect speed over quality. Fine-tuning can accidentally turn these into “rules” and then repeat them at scale.

Once a model is fine-tuned on messy, mixed data, the mistakes don’t stay small. One wrong choice gets repeated everywhere, across thousands of strings, so you end up fixing a pattern.

And when someone asks, “Why did it translate it like that?”, the honest answer is usually, “Because it learned it from the training data,” which doesn’t help you pinpoint what to change.

How RAG minimizes the risk of style bleed

Fine-tuning can cause contextual contamination because the model internalizes whatever patterns exist in the training data (including outdated terms, mixed domains, and inconsistent edits) and then applies them in the wrong situations.

RAG (retrieval-augmented generation) helps reduce style bleed by grounding each translation in the most relevant approved references (translation memory, glossary, and style guidance) at runtime, instead of relying on what the model happened to “learn” in the past.

Here’s an overview of the key differences between the two approaches:

RAG (retrieval-augmented generation) helps prevent style bleed by making context explicit. Before the model translates, it retrieves the most relevant references for that exact piece of content. This can include approved translation memory matches, required glossary terms, and the style guidance that applies to that domain.

Then it generates the translation using those references as guardrails. 

The model doesn’t need to guess what tone to use, because the right tone is part of the input.

This also makes change manageable. If your terminology or voice changes, you update the sources of truth (TM, glossary, style rules), and the next translation follows the update immediately. You don’t retrain a model and hope the new behavior “sticks.” RAG keeps the model flexible while keeping your output consistent.

Next, the question becomes: how do you apply this consistently across different domains and content types?

How Custom AI Profiles work in Lokalise

In Lokalise, Custom AI Profiles are a way to control AI translation without teaching the model one blended company style. Instead, you set up separate profiles for separate domains. For example, one for UI strings, one for marketing pages, one for help center articles, and a separate one for legal content. Each profile has its own rules and its own sources of truth.

When a profile runs, it doesn’t rely on what the AI “remembers” from training. It pulls in the context that matters for that specific domain and that specific string. This includes things like translation memory matches and glossary terms. It pulls examples from past translations as context.

This is the important part: AI is guided by today’s approved language, not whatever happened to be common in last quarter’s dataset.

How Custom AI Profiles promote domain consistency

Custom AI Profiles solve a practical enterprise problem. You need AI translations to be consistent, but you can’t afford one average style across every type of content. Profiles keep each domain strict in its own way, so UI stays UI, marketing stays marketing, and style doesn’t bleed across.

Each domain gets its own lane

Custom AI Profiles are built around the idea that different content needs different rules. For example:

• UI strings should be short, consistent, and terminology-first
• Marketing copy can be more expressive, but still needs to stay on brand
• Help-center content should be clear, instructional, and predictable

Fine tuning can still bring you personalized results. It’s a way to customize AI but at Lokalise, we believe RAG provides better and more reliable results. Profiles prevent that by letting you define the domain up front and apply the right expectations every time. 
 

RAG keeps translations grounded in approved language

AI Profiles don’t rely on what the model happened to learn in the past. They retrieve past examples continuously. This means that, if you continue to update your TM and tag the most relevant translations, it has a very good source of data that is updated constantly.

In other words, with RAG, the model is anchored in your approved phrasing and terminology before it generates anything. More importantly, the retrieval corpus is live (TM, glossary, style guide as they are today), not a static snapshot.

Guardrails and scoring make the output predictable

Context alone helps, but enterprise teams also need reliability. Custom AI Profiles add quality controls that evaluate output against the rules you care about: terminology adherence, tone constraints, and other profile-specific expectations. Lokalise is designed to score quality after the translation (LQA). 

Why the fine-tuning trap hits fintech and healthcare first

In fintech and healthcare, translations have to be exact, consistent, and defensible. That’s where generic fine-tuning can be risky. It can create fluent output while mixing contexts in the background without you even noticing. You can’t afford this in regulated, high-trust industries.

In fintech, small wording changes can change meaning

Fintech content is full of terms where “close enough” isn’t good enough. Think fees, rates, risk disclosures, onboarding flows, and transaction states. If a model learns from a blended dataset you get contextual contamination in places that matter.
It might reuse phrasing that belonged to another product line or an older release, and it does it confidently. Or it can mix different markets or apply different compliance wording.

Dynamic context orchestration avoids this because the translation is grounded in what’s approved today. With RAG, the model pulls the right references at runtime. It’s the latest version of your glossary terms for financial concepts, your preferred wording from translation memory, and the style guidance for that content type.

In healthcare, “almost right” can become unsafe

Healthcare localization has the same problem, with higher stakes. Patient-facing content, instructions, warnings, contraindications, and consent language all depend on precise wording and consistency.

Generic fine-tuning can unintentionally learn inconsistent edits, outdated phrasing, or mixed clinical vs. marketing tone, and then apply those patterns where they don’t belong.

This is where RAG and domain controls matter. When the model is guided by the right references for the specific domain (approved terminology, validated phrasing, the right tone constraints) you’re relying on your current sources of truth.

With RAG and Custom AI Profiles, AI translations can reach human-level accuracy, with around 90-95% first-pass acceptance in internal and customer evaluations.

Test Custom AI Profiles for yourself

Custom AI Profiles in Lokalise give you the structure enterprises actually need: domain-specific rules, predictable output, and updates that don’t require retraining cycles.

Sign up for a free 14-day trial to create multiple Custom AI Profiles and test them out. If you want to learn more, watch a webinar and discover how to:

• Deliver up to 95% publish-ready translations
• Personalize AI with your own past translations and localization assets
• Move beyond biased spot checks and evaluate AI quality with data
• Set up custom voices across different use cases in your business