Training
Core ConceptsThe process of feeding data to an AI model so it learns patterns and builds its capabilities — the foundation of all machine learning.
Training is how AI models acquire their abilities. During training, the model processes enormous amounts of data, adjusts its parameters to capture patterns, and gradually improves its performance on target tasks.
Training happens at multiple stages: pre-training (learning general knowledge from massive datasets), fine-tuning (specializing for specific tasks), and alignment training (RLHF or Constitutional AI to make the model helpful and safe). Each stage shapes the model's behavior differently.
Training frontier AI models is extraordinarily resource-intensive. It requires specialized hardware (thousands of high-end GPUs), massive datasets (trillions of tokens), sophisticated engineering, and months of compute time. This is why only a handful of organizations build models from scratch — most companies fine-tune existing models or use them via APIs.
Real-World Example
Training GPT-4 reportedly cost over $100 million and used thousands of NVIDIA GPUs running for months — which is why building competitive frontier models requires massive investment.
Related Terms
More in Core Concepts
FAQ
What is Training?
The process of feeding data to an AI model so it learns patterns and builds its capabilities — the foundation of all machine learning.
How is Training used in practice?
Training GPT-4 reportedly cost over $100 million and used thousands of NVIDIA GPUs running for months — which is why building competitive frontier models requires massive investment.
What concepts are related to Training?
Key related concepts include Pre-training, Fine-tuning, RLHF (Reinforcement Learning from Human Feedback), GPU (Graphics Processing Unit), Parameters, Training Data. Understanding these together gives a more complete picture of how Training fits into the AI landscape.