With the rise of large language models (LLMs) and their so well known translation capabilities, one big question keeps popping up: Do you still need a computer assisted translation (CAT) tool to take your product global?
As companies race to internationalize, some are tempted to skip foundational steps. But here’s the truth: going global is more than just translating strings.
Let’s talk about what you really need to start your global journey the right way.
Even if you think that your app/product won’t go global just yet there are some things you must get right from day one:
- Do not hardcode content in the code: Even if you only work in one language today, keep all content in external resource files. This not only makes updates easier, but also prepares your product for localization when the time comes.
- Use proper internationalization (I18n) libraries to display dates, currencies,etc.: Formatting for dates, times, numbers, and currencies differs widely across markets. I18n libraries help you avoid costly mistakes.
- Handle properly the names of your customers: Assume that even in the US not all users follow the [First Last] structure. Names vary dramatically by culture.
After that, you will be in a good shape to start your global journey.
When the time comes, and you haven’t followed the above advice, you’ll need real effort from your engineering team to globalize your product, or you’ll need a partner like Lingoport to help you tackle the internationalization challenges.
Once your product is ready for global reach, the original question becomes even more relevant:
- Do I really need a system to manage localization, or can I just rely on LLMs to translate content?
Here’s the short answer: No, you can’t rely solely on LLMs.
And here’s why:
Economical reasons: Every LLM request has a cost. Translation memories (TMs) and databases help reduce duplication. Making localization cheaper and more scalable.
Consistency: Even similar phrases can benefit from reuse. CAT tools help ensure consistent voice and terminology across your product and this is something that LLMs struggle with.
Locale and content type performance: LLM quality differs by language and content type. For less-supported languages, you’ll likely need human support and a CAT tool, or a tool like blackbird.io, helps manage that complexity.
No one size fits all: This is quite basic and important: Not all the content is created equal.
- Marketing copy → human translation
- UI strings → it depends
- User generated content → machine translation
CAT tools help you assign the right quality level to the right content, easily. Tools like Inten.to and ModelFront can also help evaluate MT quality and flag what needs human review.
As you see, there are multiple reasons why to keep some type of content management tool instead of leaving everything to be handled with just pure machine translation without a translation memory. And the good news is: You don’t have to start with a paid solution. Some great open-source localization tools include:
- Tolgee
- Pontoon
- Weblate
- i18n-Tasks
- Traduora
These tools can help you ramp up with minimal investment while keeping full control of your content.
Obviously all the responsibility will be in your team, so this is not something that you should take lightly.
Eventually, if your product and team scale, you might want to upgrade to a more full-featured CAT tool aka translation management platform. To name a few: Smartling, Phrase, XTM, BureauWorks, Lilt, etc. Each one has its strengths depending on your workflow, team structure, and scale.
To summarize, don’t be tempted by the siren song of “LLMs can do it all.” They’re powerful, no doubt about that, and can make your localization process faster and more efficient; from refining translations generated via CAT tools by checking for tone, clarity, and fluency, to validating adherence to instructions, terminology, and style guides.
In fact, we’ve written multiple articles about that:
- Using LLMs to review source content before translation
- Automating translations using Json scripts
- Use of metadata in conjunction with LLMs to improve MT output
But to maintain quality across regions, you still need structure, strategy, and tools.
LLMs are a powerful augmentation layer. Not a full replacement.
Start with a strong foundation, and build up the right stack. Your global users will thank you for it.
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