Basic Principles and Technical Infrastructure

CONVERSATIONAL AI

Large language models (LLMs) are rapidly advancing toward becoming the “new office colleague” in the corporate world. But what technical principles must these models rely on to truly be effective? Is it enough to just be a “model trained on big data”? Of course not. At CBOT, when developing LLM-based solutions, we systematically apply the core principles that ensure a model is effective, secure, and sustainable. In this article, we explain not how LLMs work, but how they work robustly.

  1. Data-Driven Training: You Must Be Careful What You Teach
    An LLM’s success is determined not just by the algorithm, but by the data it’s trained on. The quality of the training data is as important as its quantity. Instead of random texts, meaningful, balanced, up-to-date, and representative data should be used.
    At CBOT, we apply this principle in two ways:

  • Pre-training with public data: We use pre-trained models trained on publicly available, linguistically diverse sources to help the model learn the general rules of language.

  • Fine-tuning with organization-specific data: We retrain the model with an organization’s own texts (customer correspondence, instructions, documents) to tailor outputs to the sectoral and corporate context.

Ensuring that the data is free of ethical concerns, compliant with regulations, and stripped of linguistic biases is also among our top priorities in this process.

  1. Transformer Architecture: A Structure That Understands Context
    LLMs, thanks to the transformer architecture, begin to understand not only words but also the context between sentences, even the tone and intent of the text. The transformer has two core capabilities:

  • Attention mechanism: Allows the model to “pay attention” to other parts of the text when evaluating a word.

  • Parallel processing: Evaluates the text in chunks rather than sequentially, significantly shortening the training time.

The models used by CBOT are structured to optimize this architecture, achieving greater efficiency with fewer resources. In other words, instead of blindly enlarging the model, we ensure efficiency through smart model design.

  1. Parameter and Layer Structure: Depth Matters
    Every LLM contains a large number of mathematical parameters. These parameters represent the knowledge the model has learned. The “size” of a model is typically measured by the number of these parameters. However, what’s critical here is not just the “number of parameters” but how these parameters are organized.

  2. Security and Control: Model Predictions Must Be Manageable
    Large language models know a lot, but they don’t always tell the truth. This behavior, known as “hallucination,” arises when the model produces answers even on topics it’s unsure about. This can pose serious risks in corporate use.
    CBOT manages this risk through the following methods:

  • Controlled output generation: Ensuring the model produces answers only for specific tasks.

  • Content filtering layers: Filtering responses according to predefined rules.

  • Confidence score calculation: Each output comes with statistical confidence levels.

  • Human-approved workflows: In critical scenarios, the final decision is left to the user.

In corporate projects, the success of systems managing artificial intelligence is often more important than the AI itself.

  1. Real-Time and High-Performance Infrastructure
    An LLM generates value not only by working correctly but also by working quickly and seamlessly. Especially in scenarios like customer interaction, call centers, or document processing, milliseconds can be critical.
    CBOT’s infrastructure provides:

  • Cloud-based flexible architecture: Capacity automatically increases as demand rises.

  • Easy integration via API: Seamlessly adapts to existing systems.

  • On-premise usage option: Special server setups for projects where data must not leave the premises due to regulations.

  • Optimized model weights: Special models balancing speed and cost.

Conclusion: Solid Foundation, Strong Application


LLMs are among the most exciting components of generative AI. However, bringing these models into the corporate environment requires a solid technical infrastructure and an engineering process based on clear principles. At CBOT, we don’t just use models; we adapt them to corporate needs, secure them, and integrate them into business processes.
For an LLM to add value to an organization, it’s not about being big but about being properly structured. That’s precisely where our expertise begins.