Pre-training
LLM & Language ModelsThe initial training phase where an AI model learns general knowledge from massive datasets before being specialized for specific tasks.
Pre-training is the first and most expensive phase of building an AI model. During pre-training, the model processes enormous amounts of data (trillions of tokens for large LLMs) and learns general patterns: language structure, world knowledge, reasoning patterns, coding syntax, and more.
For LLMs, pre-training typically involves predicting the next token in a sequence. The model reads billions of web pages, books, code repositories, and other text, learning to predict what comes next. This simple objective, at massive scale, produces remarkably capable models.
Pre-training is astronomically expensive. Training GPT-4 reportedly cost over $100 million in compute alone. This is why only a handful of well-funded organizations can build frontier models from scratch. Most AI applications fine-tune pre-trained models rather than training from scratch.
Real-World Example
When Anthropic pre-trains Claude they feed it trillions of tokens of text — the model learns general language understanding before being fine-tuned for helpfulness and safety.
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What is Pre-training?
The initial training phase where an AI model learns general knowledge from massive datasets before being specialized for specific tasks.
How is Pre-training used in practice?
When Anthropic pre-trains Claude they feed it trillions of tokens of text — the model learns general language understanding before being fine-tuned for helpfulness and safety.
What concepts are related to Pre-training?
Key related concepts include Fine-tuning, Foundation Model, Training Data, Token, Transfer Learning. Understanding these together gives a more complete picture of how Pre-training fits into the AI landscape.