ChatGPT is trained using machine learning, particularly a method called transformer-based deep learning. During training, the model processes vast amounts of text from books, scientific articles, websites, and other sources. It learns patterns of language, associations between words, and typical structures of explanations. However, it does not “understand” in the human sense but instead calculates probabilities of which word or phrase should come next.
The Role of Human Feedback
After the initial training, ChatGPT is improved using Reinforcement Learning from Human Feedback (RLHF). Human reviewers check answers and provide feedback on whether they are accurate, clear, and helpful. This helps the AI align its responses with human expectations of truthfulness and reliability.
Truth Versus Probability
ChatGPT does not possess absolute knowledge of truth. Instead, it calculates what is most likely correct based on patterns in data and its training. When a user asks a factual question, the model draws from its stored knowledge and matches it with the most consistent and verified information it has encountered.
Handling Conflicting Information
If training data contains contradictory sources, ChatGPT may sometimes produce uncertain or inconsistent answers. To address this, it tends to prefer widely accepted scientific consensus or verified sources. It can also clarify that certain topics are debated and present multiple perspectives.
The Limits of AI Judgment
Unlike humans, ChatGPT does not have beliefs or direct access to reality. It cannot independently verify new events unless connected to updated information sources. Its responses are limited by the data it was trained on and the ability to reason probabilistically.
Why It Still Makes Mistakes
Errors occur when the model overgeneralizes patterns or when reliable data is scarce. Since it relies on probability rather than direct verification, it can sometimes produce hallucinations — plausible but incorrect statements. This is why cross-checking with reliable references remains important.
Conclusion
ChatGPT learns from massive datasets and human feedback, using probabilities to determine what is most likely true. While it often provides accurate and useful answers, it does not truly “know” reality. Its strength lies in summarizing, explaining, and simulating reasoning, but human verification is always needed for critical decisions.
Glossary
- Machine learning – computer systems learning patterns from data.
- Transformer – a neural network architecture used in language models.
- Reinforcement Learning from Human Feedback (RLHF) – method where human reviewers guide AI to improve its responses.
- Probability – likelihood that a certain answer is correct.
- Hallucination – AI-generated but false or misleading information.
- Scientific consensus – general agreement among scientists based on evidence.
