What is an LLM and How Does It Work?

AI

Generative artificial intelligence has become the most talked-about technology topic of recent times. It’s used across a wide range, from chatbots to document summarization, text generation to call center integrations. So, what is the real power behind these applications? The answer: LLMs, or Large Language Models. These models not only generate information; they also add knowledge-based intelligence to organizations’ business processes. At CBOT, we both develop these models and integrate them according to the specific needs of organizations. In this article, we explain the question “What is an LLM and how does it work?” clearly, without getting lost in technical details.

What is an LLM?

An LLM (Large Language Model) is an artificial intelligence model trained on vast amounts of text data, with the ability to understand and generate human language. The main goal of these models is to produce meaningful and contextually appropriate output in response to a given text input. For example: to the question “How’s the weather today?”, it can give a meaningful response like “It’s partly cloudy in Istanbul today.”

At CBOT, we develop LLM-based solutions that can analyze large volumes of data from various sectors and generate organization-specific outputs from this data. We optimize these models to deliver strong performance in the Turkish language and adapt them to comply with regulations and corporate culture.

How Do LLMs Work?

Large language models use a special neural network architecture called a “transformer.” The transformer architecture is particularly successful at understanding and preserving context among data. LLMs process texts not just word by word but within the whole meaning.

The working logic is essentially based on three steps:

Pretraining: The model is trained on very large text datasets. In this phase, the model learns the structure of the language, syntax, and relationships between words. But this training is general, not specific to any organization or sector.

Fine-tuning: In this phase, the model is retrained for a specific task or sector. For example, an LLM used in the finance sector learns sector-specific terminology, document structures, and customer interactions. At CBOT, we manage this fine-tuning process for organizations, making general models sectorally competent.

Prompt Engineering: An important part of understanding how the model works is giving it the right commands. LLMs are not designed to always provide the best answer to every question, but rather to generate the most probable response to a given input. That’s why one of CBOT’s areas of expertise is prompt engineering, which enables effective and accurate communication with the model.

Why Are These Models Called “Large”?

Two key factors are important for an LLM to be considered “large”:

  • Number of parameters: This determines the model’s learning capacity. Modern LLMs contain billions of parameters. This enables them to better grasp the complex structure of language.
  • Size of training data: LLMs are trained on datasets with hundreds of billions of words. This allows them to learn not only information but also complex aspects such as cultural context, tone, and even metaphorical meanings.

The solutions developed by CBOT are designed to turn this “largeness” into benefits for organizations. A model can be large, but for this power to be useful, it needs guidance, control, and integration.

Corporate Application Areas of LLMs

CBOT’s field experience shows that LLMs make a difference in many critical areas within the corporate world. Some key application areas include:

  • Automated document processing and summarization
  • Chatbots that can generate meaningful responses to customer requests
  • Information extraction for decision support systems
  • Natural language interaction with corporate databases
  • Creative text generation and automation in internal communication

In all these areas, LLMs play a complementary and accelerating role alongside human labor. However, the most important point is this: instead of using these models directly, they need to be integrated into the organization’s language, culture, and data. CBOT’s expertise lies in realizing this integration with a strategic perspective.

Conclusion: LLMs Are More Than Just Technology

LLMs are not merely artificial intelligence algorithms doing language modeling; they are a new foundational infrastructure driving the digital transformation of the business world. Building and effectively managing this infrastructure is critical for long-term competitive advantage. At CBOT, we go beyond the technical details of LLM technology, transforming it into organization-specific, secure, scalable, and results-oriented solutions.

The AI strategies of today and tomorrow will be built on a strong LLM infrastructure. And this journey requires not just the right technology, but also the right business partner.