In our previous strategy and readiness blog, we explored the benefits and challenges of training an LLM (large language model) to support your localization strategy. We posed some initial questions to help you decide if a customized machine translation solution is right for you. Would the rewards justify the effort, and is your business ready to commit the necessary time and resources?
If you’ve identified your business needs, defined your objectives and success criteria, and secured the resources you need, it’s time to think about the practicalities.
Here are the questions you need to ask to make sure your LLM training process goes smoothly and delivers real results.
1. How will this integrate into your existing workflow?
Customizing your LLM should make it easier to achieve quality results, not slow you down. Check your chosen tool is compatible with the platforms you use to author, translate, and publish content. Different models may work better for different markets—Rubric can help you select the right one, train it effectively, and connect it to your existing workflow.
2. What are the security, privacy, and compliance requirements?
Before feeding data into any LLM, be sure you understand how it will be used and shared. Public LLMs could expose company or customer data, or put intellectual property at risk. If you’re buying a bespoke product, ask your vendor about their security policies.
Whether you’re refining an existing LLM or building a custom model, good governance is vital to ensure it delivers results you can trust.
3. Do you have the internal expertise to build and maintain this?
Exact requirements will vary depending on your goals, but you’ll likely need a mix of localization and AI expertise—relying on one skillset alone won’t be enough. You might build a team including prompt engineers, linguists, and quality checkers, among others. If you don’t already have those skills in house, it could be more cost effective to outsource your LLM training or customization.
4. What is your timeline?
A custom LLM could save you time in the long run, but implementing it is no quick fix. Be realistic about the time you’ll need to prepare your data, train the model, evaluate results, and educate teams.
5. Should you build, buy, or partner?
Building your own LLM gives you full control of the parameters, but it’s a big investment of time, money, and expertise. Do you have the resources, and is it future-proof in the face of budget or people changes? Buying in a solution is low effort, but beware hidden costs. What level of support is included? How much can the tool be customized—and at what cost? What about licensing fees?
A partnership model can offer the best of both worlds—you’ll have custom flexibility and support, without paying for more than you need. Rubric is as hands-on or hands-off as you like. We can support with terminology management and glossary development, quality assurance, and system integration. And you’ll have a dedicated contact to talk through any concerns and tailor a solution for you.
6. What ROI can you realistically expect from training an LLM or a custom MT solution?
The investment required to train or customize a solution means you’ll only see a return if it’s used at scale. Are your content volumes high enough to justify the spend? Would another option, like post-editing, deliver similar results for less?
Identify where you want to make savings, such as faster turnarounds or reduced rework. Contrast this with your end-to-end costs, depending on whether you want to purchase, partner, or keep everything in house.
Rubric assesses where you are and models the costs and benefits of each option, so you can decide whether a custom LLM is the right investment for your global content. We work with you to design a cost-effective, scalable, and sustainable solution that delivers ongoing savings and improvements.
Are you ready to start training your LLM the right way? Smarter AI starts with smarter questions—so talk to us, and let’s build a solution together.