Model Training and Fine-Tuning

AI, CONVERSATIONAL AI

One of the first concepts that comes to mind when talking about artificial intelligence is now large language models. The capabilities these models offer are impressive; however, the training process behind the scenes is often perceived as a “black box.” Pre-training or fine-tuning? When and how should each be used? How should organizations leverage their own datasets? Which approach is more effective, safer, and more cost-efficient?

At CBOT, we encounter each of these questions directly in our projects. Because every customer, every industry, and every use case is different. In this article, we clarify the concepts of model training and fine-tuning, and explain how we strategically combine these two techniques through CBOT’s enterprise approach.

Model Training: A Strong Foundation

Model training (pre-training) refers to the initial phase in which large language models learn the structure of language, patterns, and contextual relationships. In this phase, models are trained on massive datasets from a wide range of sources. The goal is for the model to grasp the general logic of the language. In short, at this stage, the model “learns a bit of everything” but “masters nothing completely.”

Fine-Tuning: Industry-Specific Deepening

Fine-tuning means retraining a pre-trained model with a smaller but domain-specific dataset. Our goal is to help this generally knowledgeable model better understand a specific task. For example, if we are developing a virtual assistant for the financial sector, we fine-tune the model with terms, regulations, and customer interactions related to banking. In this way, the model fully adapts to the language of the industry and the needs of the institution.

With fine-tuning:

  • You can customize general models to fit your organization’s needs,

  • You can achieve faster results with less data,

  • You can keep costs under control,

  • You can operate in a more controlled manner in terms of data security and regulatory compliance.

So, Is Fine-Tuning Always the Answer?

No. Fine-tuning is powerful, but it may not be the best option for every situation. If you don’t have labeled data or need real-time information, alternatives like RAG (retrieval-augmented generation) or prompt engineering should also be considered.

Model training and fine-tuning form the backbone of AI projects. While pre-training prepares the model for the general structure of the language, fine-tuning adapts it to the real needs of the business world. At CBOT, we consider these two processes together and build industry-specific solutions on the strongest foundation.

Every organization’s AI journey is unique. However, when the right model, the right data, and the right method come together, this journey becomes not only efficient but also sustainable. As CBOT, we continue to be a trusted companion on this journey.