The fine-tuning trap in AI translation

Fine-tuning AI models for translation can lead to "contextual contamination," where the model inadvertently incorporates outdated or inconsistent data from mixed sources, resulting in translations that are not aligned with current brand standards. This is particularly problematic in industries like fintech and healthcare, where precision is critical. Dynamic context orchestration and retrieval-augmented generation (RAG) offer solutions by using runtime references such as translation memory, glossaries, and style guides, ensuring translations remain consistent and relevant without retraining cycles. Lokalise's Custom AI Profiles allow enterprises to maintain domain-specific rules, providing predictable and quality-controlled translations that adapt to real-time changes without compromising on consistency across different content types.