The simplified pipeline
1. Data
Text, images, audio and other material are prepared and filtered.
2. Tokens
Language is converted into smaller units that models process mathematically.
3. Transformer
Attention mechanisms learn relationships between information and context.
4. Pre-training
The model predicts sequences and adjusts internal numerical weights.
5. Post-training
Feedback, demonstrations and reinforcement learning improve usefulness and safety.
6. Deployment
Search, tools, retrieval, memory and product systems shape the user experience.
Why does AI seem to understand?
Modern language models learn statistical relationships across very large amounts of information. When you ask a question, the system uses its learned weights plus the current context to generate a response token by token.
Important: A deployed model generally does not retrain itself after every conversation. Improvements usually arrive through new model versions, better data, stronger training methods, tools and evaluation.
References
OpenAI — How foundation models are developed
Attention Is All You Need