Fine-Tuning Open-Source AI Models: A Practical Vietnamese Data Strategy

30/08/2026

The Biggest AI Question for Vietnamese Businesses

"Why can't I just use existing large models immediately, and why must I incur the cost of fine-tuning a custom AI model for my company?" The straightforward answer is: General models understand basic Vietnamese, but they do not know your specific "business playbook." Without contextual Vietnamese data, AI will produce generic or inaccurate responses in specialized scenarios like handling complaints or analyzing contracts.

I have sat down with dozens of operations directors, from retail chains to manufacturing plants, to address this issue. They all want AI that understands Vietnamese like an "insider," not like a translation student. This article is not about empty theory. We will dive straight into the practical process: before starting, during fine-tuning, and after deployment.

Before Starting: Data Preparation and Platform Selection

Many businesses believe that having data is enough to start immediately. Wrong. Dirty data creates a "dirty" AI. Before considering AI model fine-tuning, you must review your existing data repository. Do you have sufficient internal manuals, meeting minutes, and historical customer support chats written in natural Vietnamese?

For open-source models like Llama 3 or Mistral, this step is critical. If you want AI to understand lubricant industry terminology or import-export procedures at Thai ports, you must have that data. I often see projects delayed by one-third of the timeline solely because input data was fragmented, inconsistent in spelling, or contained excessive redundant information.

Platform selection also requires careful consideration. Not every model fits every need. If your company needs to handle short texts or quick chats, Mistral might be a lighter option. If you need to analyze long contracts with complex logic, Llama 3 is the preferred choice. At AIVISION, we often advise starting with an existing model, evaluating its capabilities on a test dataset before deciding to invest in infrastructure for training.

During the Process: Fine-Tuning Workflow and Risks to Avoid

The AI model fine-tuning process is not a one-time code run. It is a series of loops: train, evaluate, adjust, and retrain. You must define clear objectives. Do you want the AI to respond faster, or do you want it to strictly adhere to your company's legal compliance procedures?

The biggest risk at this stage is "catastrophic forgetting." When you teach the AI too much new knowledge, it may forget its inherent general knowledge. For example, a model fine-tuned too deeply for the instant noodle industry might perform poorly on general technical issues or adopt an overly rigid writing style.

We need to balance original training data with new data. A good strategy is to retain about 10-20% of general data to maintain language capabilities, while focusing the rest on industry-specific data. For partners like Masan or Meat Deli, ensuring the AI understands corporate culture and internal terminology is the top priority, rather than knowing everything in the world.

After Deployment: Evaluation and Continuous Fine-Tuning

Many believe that once deployed, the job is done. This is a fatal mistake. Vietnamese data changes rapidly, with new terms emerging daily. A model fine-tuned today could become obsolete in six months without updates.

You need to establish a monitoring process. Track instances where the AI answers incorrectly, lacks confidence, or refuses to answer. These errors are valuable raw material for the next training loop. Do not be afraid of AI errors in the early stages, as long as you have a mechanism to collect and correct them.

Operational costs are also a challenge. A well-fine-tuned model can save twice the cost compared to hiring staff for manual processing, but it also demands computational resources. Start small, running in parallel with humans to compare effectiveness. In projects with TTN or beer industry companies, we found that quarterly continuous fine-tuning yields better results than one large annual training session.

Trade-offs Between Cost and Effectiveness in Practice

No solution is perfect. Using open-source LLMs and fine-tuning your own AI models offers flexibility and absolute data control, but it requires a strong technical team and significant initial investment time. Conversely, ready-made paid APIs are easy to deploy but difficult to deeply customize for specific business operations, and costs increase with the number of calls.

For businesses with highly proprietary data, such as Gene Solutions in the biological field, self-hosting and fine-tuning are mandatory for security. For retail chains in Mexico or the Philippines, where speed and rapid scalability are needed, weighing the options between compact models running on the edge and large models on the cloud is essential.

Do not try to do everything immediately. Start with a specific pain point: a customer support chatbot or report summarization. Once you have mastered the AI model fine-tuning process for a small segment, expand to others. Patience and practicality are key factors.

Frequently Asked Questions

Do I need millions of Vietnamese data records?

Not necessarily. With modern fine-tuning techniques like LoRA, you only need a few hundred to a few thousand high-quality question-answer pairs, carefully curated, to create a significant difference compared to the base model.

What is the cost of fine-tuning an AI model on Llama 3?

Costs depend on the model size and the number of training iterations. It can range from a few million to tens of millions of VND per iteration, excluding personnel costs. However, compared to the cost of operational errors or outsourcing, this investment usually pays off quickly.

Can a fine-tuned AI learn continuously without intervention?

Do not let the AI learn entirely on its own. Unsupervised learning on real-time data is prone to noise and can cause the model to "go crazy." There must be a human review and confirmation process for new data before retraining.

AIVISION helps enterprises turn AI into working systems. Explore our enterprise AI solutions, read more on the AIVISION blog, or talk to our team about your own use case.