Inference
Pretraining
The first and most expensive stage of building a language model, where it learns to predict the next token across a huge unlabeled corpus of web text, books, and code.
In depth
The first and most expensive stage of building a language model, where it learns to predict the next token across a huge unlabeled corpus of web text, books, and code. Pretraining is where the model picks up language, facts, and general reasoning ability. The result is a base model that continues text rather than following instructions. GPT-3 showed that scaling pretraining alone produces strong few-shot ability, and every modern LLM still starts this way before post-training shapes it into an assistant.
Primary source: arxiv.org
Example
In practice, developers reach for Pretraining when they need the capability described above as part of an AI feature or workflow.
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Hands-on guides, comparisons, and tutorials that cover Inference.
FAQ
Common questions
What is Pretraining?
The first and most expensive stage of building a language model, where it learns to predict the next token across a huge unlabeled corpus of web text, books, and code.
Why does Pretraining matter for AI developers?
Pretraining sits in the Inference part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Pretraining?
Developers Digest publishes tutorials and videos that cover Inference topics including Pretraining. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
Inference
Continued Pretraining
Running more next-token-prediction training on an already pretrained model using a large unlabeled corpus from a target domain, such as code, math, medicine, or a new language.
Inference
Next-Token Prediction
The training objective behind GPT-style language models: given a sequence of tokens, predict a probability distribution over the token that comes next.
Inference
Prefill
The first phase of LLM inference, where the model processes the entire input prompt in parallel and builds the KV cache before producing any output.
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