# Pre-training

> The initial phase of training AI models on large, diverse datasets to learn general patterns before specialization for specific tasks.

Category: Techniques

Source: https://chipp.ai/ai/glossary/pre-training

Pre-training is the initial, foundational phase of training an AI model on a large, diverse dataset. During pre-training, the model learns general patterns of language, knowledge, and reasoning that serve as the foundation for all downstream tasks.

For language models, pre-training involves: assembling a massive training corpus (trillions of tokens from books, websites, code, and more), training the model to predict the next token given preceding context, and running this process for weeks or months on thousands of GPUs.

Through this process, the model learns: grammar and language structure, factual knowledge about the world, reasoning and logic patterns, coding abilities, multilingual capabilities, and conversational patterns.

Pre-training is distinguished from: fine-tuning (additional training on a specific dataset after pre-training), RLHF (Reinforcement Learning from Human Feedback — aligning the model with human preferences after pre-training), and in-context learning (adapting behavior through examples in the prompt, no additional training).

Pre-training is extremely expensive. Training a frontier model like GPT-4 or Claude 3 Opus costs tens to hundreds of millions of dollars in compute. This is why only a handful of organizations train foundation models from scratch, while thousands of companies build applications on top of these pre-trained models.

For AI agent builders, pre-training has already been done by model providers (OpenAI, Anthropic, Google, Meta). Builders benefit from pre-training through the models' capabilities — language understanding, reasoning, and knowledge — which they customize through prompts, knowledge bases, and actions.

## Related Terms

- [Fine-tuning](https://chipp.ai/ai/glossary/fine-tuning.md): The process of further training a pre-trained AI model on a specific, smaller dataset to specialize it for particular tasks or domains.
- [Foundation Model](https://chipp.ai/ai/glossary/foundation-model.md): Large AI models trained on broad, diverse data that serve as the base for many different downstream applications and tasks.
- [Large Language Model (LLM)](https://chipp.ai/ai/glossary/large-language-model.md): A neural network trained on massive text datasets that can understand and generate human-like language, powering modern AI assistants and agents.
- [Transformer](https://chipp.ai/ai/glossary/transformer.md): The neural network architecture that powers modern AI language models, using self-attention mechanisms to process sequences of data in parallel.

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This term is part of the [Chipp AI Glossary](https://chipp.ai/ai/glossary), a reference of AI concepts written for builders and businesses.

Build AI agents with no code at https://chipp.ai.