LLM Models Explained: What They Are and How They Work

Introduction

Large Language Models, or LLMs, are at the heart of the AI revolution currently transforming the business world. From ChatGPT to the AI assistant running your customer service: behind virtually every smart chatbot is an LLM. But what exactly is an LLM, and how does it work under the hood?

In this article, we explain in accessible language what LLMs are, how they are trained, what their limitations are, and why they matter for business owners considering AI adoption. No mathematical formulas, just concrete insights.

What Is a Large Language Model?

A Large Language Model is a neural network trained on enormous amounts of text. The model learns patterns in language: which words frequently appear together, how sentences are structured, and which answers logically follow a question. The result is a system that can generate text nearly indistinguishable from human writing.

The word "large" refers to the number of parameters, the internal settings the model optimizes during training. Modern LLMs like GPT-4 and Claude have hundreds of billions of parameters. More parameters means the model can understand and produce more nuanced language.

It is important to understand that an LLM does not "understand" language the way a human does. It calculates statistical probabilities. When you ask a question, the model predicts word by word which answer is most likely based on its training.

How Are LLMs Trained?

LLM training happens in two phases. First is pre-training, where the model processes billions of pages of text from books, websites, scientific papers, and forums. During this phase, the model learns language structure, factual knowledge, and reasoning patterns.

Then comes fine-tuning, where the model is refined for a specific task. At OpenClaw, we apply fine-tuning to align the model with a client's specific knowledge base. This way the chatbot provides answers that are relevant and accurate for your business, rather than generic information.

Limitations and Risks

LLMs are impressive but not infallible. The best-known problem is hallucination: the model sometimes generates information that sounds convincing but is factually incorrect. This is a statistical consequence of how the model works, it chooses the most probable words, not necessarily the most correct ones.

Additionally, LLMs can contain bias present in their training data. And they have a knowledge cutoff: information published after the training date is unknown to them. OpenClaw addresses these limitations by connecting the model to a current knowledge base with verified information.

Privacy is another concern. Because LLMs are trained on publicly available text, it is important that sensitive business information does not accidentally end up in prompts. OpenClaw processes all data on European servers and does not store conversations for model training.

Why LLMs Matter for Your Business

The combination of a powerful LLM with a business-specific knowledge base makes it possible to help customers 24/7 with questions that previously could only be answered by human staff. Think product advice, technical support, or handling complaints.

With OpenClaw, you do not need to train or host an LLM yourself. The platform handles integration with state-of-the-art models and gives you the tools to manage the knowledge base, monitor performance, and improve answers based on feedback.

Conclusion

LLMs are the engine behind the current AI revolution. They are not magic, but statistically sophisticated systems that understand and produce language at a level unthinkable five years ago. For businesses considering AI adoption, understanding this technology is essential for setting realistic expectations and making informed decisions.