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 ChatGPT vs. a localization platform: Which translation solution is right for your business?
Translation used to be a bottleneck for businesses looking to grow internationally. Thanks to AI, now it can happen in seconds, which changes how teams make decisions about localization. ChatGPT can produce quick translations for many everyday needs. But when quality requirements rise or the workload grows, teams start running into familiar problems: inconsistent terminology, formatting and placeholder issues, missing context, and a lack of review workflows. That’
Updated on February 3, 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 The 5 best in-context editor (ICE) tools for localization quality
When translators work on a bunch of strings listed in spreadsheets and CSVs, they're essentially flying blind. Without seeing where these phrases appear, even experienced linguists struggle to make the right call on tone, length, and word choice. In-context editors (ICE) tools like Lokalise solve this issue by giving translators a real-time preview of the pages or apps they’re translating. So, they can see each string in the actual interface and edit it accordingly.
Updated on January 29, 2026·Shreelekha Singh 7 bold predictions for the future of localization in 2026
The localization industry is currently sitting at a high-stakes betting table. The chips are AI, automation, and data, and the jackpot is global growth. But knowing exactly where to place those bets is the difference between leading the market and playing catch-up. We didn’t want safe guesses. We gathered 7 top localization experts from major tech partners like Webflow and language service providers like Acclaro and Argos to industry veterans and strategists, and asked them to place the
Updated on January 28, 2026·Brittany Wolfe The global app launch stack: DevOps, development, and localization
Launching an app globally isn’t just about shipping a build to the App Store or Google Play. A real global release depends on the tech stack for a global app launch — the set of development, localization, and DevOps tools that let teams ship updates across multiple languages and markets at the same time. This is where many teams hit the wall. Code might be ready, but translations lag behind. Marketing texts live in spreadsheets. Releases get delayed because localization happe
Updated on January 14, 2026·Ilya Krukowski 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 
