The Birth

Creating an LLM is one of the most resource-intensive endeavors in human history. It requires more computing power than sending humans to the moon, training data representing a significant fraction of human knowledge, and teams of hundreds of researchers and engineers.

Here's how these remarkable systems come to exist.

The Journey from Data to AI

1. Phase 1: Data Collection

   Gather trillions of tokens from books, websites, code, scientific papers, and more. Quality and diversity of this data shapes everything that follows.

2. Phase 2: Pre-training

   Train the base model to predict next tokens. This takes months on thousands of GPUs and costs tens of millions of dollars.

3. Phase 3: Fine-tuning

   Train on high-quality examples of helpful, harmless conversations. Transforms the raw prediction engine into a useful assistant.

4. Phase 4: RLHF

   Human raters compare outputs, and the model learns from their preferences. This is what makes AI assistants actually helpful and safe.

Phase 1: The Data

Everything begins with training data. Modern LLMs are trained on a substantial fraction of all text that exists on the internet, plus digitized books, academic papers, and code repositories.

The composition matters as much as the size:

• Web crawls (filtered for quality)
• Digitized books and publications
• Code repositories (GitHub, etc.)
• Scientific papers and databases
• Forums, discussions, Q&A sites

Phase 2: Pre-training

Pre-training is where the model learns to predict the next token. The process is conceptually simple: show the model text, have it predict what comes next, and adjust its parameters to be slightly better at that prediction.

Repeat this trillions of times.

After pre-training, you have a "base model"—something that can complete text fluently but isn't yet useful as an assistant. It might continue your prompt but won't engage helpfully in conversation.

Phase 3: Fine-tuning

Fine-tuning teaches the base model how to be a helpful assistant. This involves training on carefully curated examples of good conversations.

These examples demonstrate the desired behavior: being helpful, accurate, clear, and appropriately cautious. The model learns to mimic these patterns.

Phase 4: RLHF

Reinforcement Learning from Human Feedback is often the secret sauce that separates impressive demos from truly useful AI assistants.

How RLHF Works

1. Generate: Model produces several different answers to the same prompt
2. Compare: Trained raters rank responses from best to worst
3. Learn: A separate reward model learns to predict human preferences
4. Optimize: Main model is trained to produce responses the reward model rates highly

The Staggering Scale

Creating a frontier LLM is among the most expensive and resource-intensive projects humans have ever undertaken:

Key Takeaways

• LLM creation has four main phases: data collection, pre-training, fine-tuning, and RLHF
• Training data quality and diversity fundamentally shape model capabilities
• Pre-training teaches language patterns; fine-tuning and RLHF shape behavior
• The scale is staggering: billions of dollars, massive energy use, years of work
• Only a few organizations can currently create frontier models

Related Concepts