AI glossary · Generative AI and LLMs
Pre-training
Pre-training is the first, biggest stage of training an AI model, where it learns general patterns from a huge dataset before being adapted for specific uses.
Also called pretraining
In more detail
For a language model, pre-training means reading a huge amount of text and learning to predict the next token. This is where most of its knowledge comes from, and it can take months on thousands of GPUs.
The result is a base model that is knowledgeable but not yet a helpful assistant. Later steps like fine-tuning and RLHF teach it to follow instructions and behave well.
Related terms
Fine-tuningFine-tuning is extra training that adapts an existing AI model to a specific task, style or field, using a smaller, focused set of examples.Reinforcement learning from human feedback (RLHF)Reinforcement learning from human feedback (RLHF) trains an AI model to give better answers by learning from people’s ratings of its responses.Training dataTraining data is the set of examples, such as text, images or past records, that an AI model learns from. Its quality and range shape what the model can do.Foundation modelA foundation model is a large AI model trained on broad data that can be adapted to many different tasks, from chat to coding to image analysis.Self-supervised learningSelf-supervised learning is when an AI model creates its own practice questions from raw data, like hiding a word in a sentence and learning to predict it.
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