ChatGPT works by predicting word for word what the most logical next word is, based on billions of texts the system has been trained on. The system responds to instructions in Dutch and generates answers that sound as if they were deliberately written, but are actually the result of quick statistical predictions.
What is ChatGPT and how does it work?
ChatGPT is a large language model (LLM) developed by OpenAI. The abbreviation stands for Chat Generative Pre-trained Transformer. Each component describes a core of how the system works: it is trained on large amounts of text, it generates new text based on that training, and it uses an architecture called “transformer”.
The difference from a search engine: ChatGPT provides a direct customised answer, not a list of links. The difference from a database: ChatGPT does not store facts, but recognises patterns in language. If you want to know more about what ChatGPT is and what you can use it for, our article on what ChatGPT is that out.
How is ChatGPT trained?
ChatGPT was trained on hundreds of billions of words from books, websites, Wikipedia and other text sources. During the training, the system learned to predict better and better which word logically follows a series of other words.
That learning was through correction. On the sentence “the sky is...” the system could initially predict any word. Every wrong prediction was corrected, every right one reinforced. After billions of repetitions, the system learned that “the sky is blue” was statistically more logical than any other combination.
The result is a model that understands language in context but does not store facts like an encyclopaedia. ChatGPT does not know that Paris is the capital of France as an established fact. It knows that those three words occur together very often in texts and generates an answer based on that.
What is the transformer architecture?
The basis of ChatGPT is the transformer architecture, introduced by Google researchers in 2017 in the paper “Attention is all you need”. This architecture enabled a breakthrough in natural language processing.
The core mechanism is called self-attention. With this, for each word, the system assesses which other words in the sentence or conversation are relevant to the meaning. For the sentence “the bank is on the bank”, the system understands via self-attention that “bank” here means a river bank and not a financial institution, because “bank” is given more weight in the context.
This makes ChatGPT strong in understanding nuance, context and longer conversations. The system doesn't just look at the previous word, but the full context at the same time.
How does ChatGPT remember a conversation?
ChatGPT remembers everything you said in an ongoing conversation. Suppose you first ask about your dog and then where to buy food, the system combines the two questions and understands that you probably mean dog food.
This memory only applies within one conversation. As soon as you start a new conversation, ChatGPT starts again with no memory of previous sessions. In paid versions, an optional memory feature is available that lets you let the system remember specific information about yourself.
Practical corollary: share relevant context at the beginning of a conversation. The more background you provide about your role, your purpose and your target audience, the better the output will match what you need.
Why does ChatGPT sometimes make mistakes?
ChatGPT makes mistakes because it recognises patterns in language, not checks facts. If a topic appears infrequently in the training data, or if the patterns in that data are incorrect, the system generates a convincing-sounding but wrong answer. This phenomenon is called “hallucination”.
The system never indicates that it is in doubt unless you explicitly ask. A wrong answer sounds as certain as a correct one. Therefore, always check critical information through other sources before using it in reports, customer contact or decision-making.
In addition, the training dates have an end date. ChatGPT has no knowledge of recent events, unless you enable the search function or provide current information yourself in your prompt. If you want to know how ChatGPT compares to other AI models and where the differences lie, our article on the types of AI a good overview.
How to use ChatGPT effectively as a professional?
The quality of your output depends almost entirely on how you formulate your instruction. ChatGPT is a pattern recogniser: if you give vague input, it generates vague output. If you give specific context, a clear goal and a desired style, the output is immediately useful.
An effective instruction contains at least three elements: your role or background, the purpose of the text or task, and the desired output. For example, “I am an HR manager and want to write an internal email to employees about the new leave policy. Write a businesslike email of no more than three paragraphs.”
Professionals who understand how ChatGPT works also know its limits. They use the system for writing, structuring and brainstorming, but not as a source of facts or as a substitute for content expertise. In the ChatGPT course from LearnLLM you will learn to build that way of working step by step, focused on your field and role.
How is ChatGPT different from other AI models?
ChatGPT is built on OpenAI's GPT series, which has evolved from GPT-2 through GPT-3 to GPT-4. Each model became larger, more accurate and more widely deployable than its predecessor.
Other major language models such as Claude from Anthropic and Google's Gemini operate on similar principles, but are trained on different datasets and with different design choices. The technical basis is largely the same for all major models: transformer architecture, training on large text corpora, and refinement via human feedback.
Want to know which model is best for your use? Our article on open source versus closed AI models helps you make that choice.
