The fine-tuning trap in AI translation
Fine-tuning sounds like the clean way to improve AI translation quality. You train the model on your content with the expectation it’ll learn your style. In practice, generic fine-tuning is where enterprise translation programs get stuck. The issue is, the model absorbs everything in the training mix. This includes old releases, mixed brands, and inconsistent phrasing, which means you end up with contextual contamination. That’s when the model starts making confident ch
Updated on February 11, 2026·Mia Comic Term base best practices: How to build a living terminology system
Most term bases fail because they live somewhere where nobody works. A spreadsheet gets created, a few people bookmark it, and then the real work happens in the editor, in Slack, in Figma, and in whatever AI tool is generating the next draft. That gap is expensive. Terminology drifts, reviewers rewrite the same phrases, and “small” naming mistakes turn into brand inconsistency in translation, SEO issues, and support tickets that shouldn’t exist. This guide covers term base best pr
Updated on February 10, 2026·Mia Comic How to automate context management in translation
Most localization teams struggle with finding the right context. Translators jump between the TMS, Jira, Figma, Slack, and old docs just to understand a single string. Reviewers approve copy without ever seeing the screen it lives on. Developers spend hours every week explaining where text appears and what it must not break. All of this context switching in localization slows releases and drives up translation rework. On paper, it looks like “bad translation.” In reality, it
Updated on February 3, 2026·Mia Comic How AI is changing design-stage localization
Designers work in Figma. Developers work in GitHub. Localization teams work in localization software. Each team operates in isolation, creating context-switching delays that slow down your launch timelines. Design-stage localization changes this equation bringing translation into the design phase. And with AI integrated into your setup, this process can become even faster. Instead of waiting days for translations, designers get AI-translated strings in minu
Updated on January 29, 2026·Shreelekha Singh ChatGPT vs. machine translation: Is it a fair comparison?
Most teams today are asking themselves: Should we use ChatGPT or machine translation for localization? The real question should be: How can we get the best results from both, without wasting time or money? Large Language Models (LLMs) like ChatGPT are changing the way we approach translation. They offer fluency, creativity, and context awareness that traditional Neural Machine Translation (NMT) engines can’t always match. However… To get localization-ready output fro
Updated on January 30, 2026·Mia Comic Season 1, Episode 2: AI amnesia, health tech, and why humans are hard to emulate
In this episode of AI Navigators, we sit down with Sasho Savkov, Engineering Manager for the AI/ML team at Lokalise. With a PhD in clinical information extraction and nearly a decade building healthcare solutions, Sasho brings a unique perspective on what’s actually working versus what’s just noise. He challenges one of the biggest assumptions in AI today: that current single-shot learning approaches will lead us to human-level intelligence. His insights rev
Updated on September 9, 2025·Rachel Wolff RAG vs the buzz: How Retrieval-Augmented Generation is quietly disrupting AI
As a Product Manager leading AI innovations at Lokalise, I’ve been closely following the latest AI news and filtering out the noise that inevitably comes with a revolutionary tech boom. AI has moved incredibly fast since ChatGPT exploded into the mainstream in late 2022, what I like to call ‘the GPT moment’. We’ve seen major model releases roughly every few months, from GPT-3.5 through GPT-4, GPT-4o, and most recently GPT-5 with its integrated reasoning capabilities launched in Au
Updated on September 12, 2025·Adam Soltys AI translation quality achieves human parity: Is this the end of language barriers?
To paraphrase Captain Kirk, AI has boldly gone where no machine has gone before: it has finally reached human-level translation quality. But this doesn’t mean we’re done with AI translation. Far from it. We’re only just getting started, and the possibilities are both endless and exciting. After decades of clunky, error-prone, and literal-sounding machine translation, we’ve reached a pivotal moment where automated translation matches human quality. When fed t
Updated on August 13, 2025·Rachel Wolff 
