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Training an LLM

Training a large language model requires enormous amounts of data and computing power. Understanding the basic process explains why LLMs know what they know and why their knowledge has a cutoff date.

10 min•By Priygop Team•Updated 2026

The LLM Training Process: Simplified

Step 1 - Data collection: gather hundreds of billions of words from books, websites, code, scientific papers, and other text sources
Step 2 - Tokenization: convert all text into tokens the model can process
Step 3 - Pre-training: train the model to predict the next token across the entire dataset, billions of times
Step 4 - Fine-tuning: train the model further on specific tasks or high-quality examples to improve usefulness
Step 5 - RLHF (Reinforcement Learning from Human Feedback): human raters evaluate responses and the model is further trained to produce responses humans prefer
Step 6 - Safety testing: test for harmful outputs and add guardrails to prevent them
Step 7 - Deployment: make the trained model available via applications and APIs

Practice Task

Note

Think about what this training process means for practical use. If an LLM was trained on data collected up to January 2024: (1) What would it not know about? (2) What types of questions would give you the most reliable answers? (3) What types of questions would require you to verify the answer? Write your thoughts before reading the next section.

Diagram

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Deep Learning ⊂ Machine Learning ⊂ Artificial Intelligence

Key Takeaways

Training a large language model requires enormous amounts of data and computing power.
Step 1 - Data collection: gather hundreds of billions of words from books, websites, code, scientific papers, and other text sources
Step 2 - Tokenization: convert all text into tokens the model can process
Step 3 - Pre-training: train the model to predict the next token across the entire dataset, billions of times

Topics in This Module

The LLM Training Process: Simplified

Step 1 - Data collection: gather hundreds of billions of words from books, websites, code, scientific papers, and other text sources

Step 2 - Tokenization: convert all text into tokens the model can process

Step 3 - Pre-training: train the model to predict the next token across the entire dataset, billions of times

Step 4 - Fine-tuning: train the model further on specific tasks or high-quality examples to improve usefulness

Step 5 - RLHF (Reinforcement Learning from Human Feedback): human raters evaluate responses and the model is further trained to produce responses humans prefer

Step 6 - Safety testing: test for harmful outputs and add guardrails to prevent them