How LLMs Are Trained: From Raw Text to ChatGPT

Building a large language model like ChatGPT or Claude is one of the most complex engineering projects in technology. It requires vast datasets, enormous compute resources, sophisticated training algorithms, and careful human oversight. The process from raw text to a helpful AI assistant involves multiple distinct stages, each serving a critical purpose.

Stage 1: Data Collection and Preparation

Everything starts with data. Modern LLMs are trained on trillions of tokens drawn from diverse sources:

• Web crawls: Common Crawl and similar datasets provide billions of web pages, representing the broadest source of text
• Books: Digitized books provide long-form, well-edited text across every subject
• Code repositories: GitHub and similar sources provide programming knowledge in dozens of languages
• Academic papers: ArXiv, PubMed, and other repositories provide scientific and technical knowledge
• Wikipedia: A high-quality encyclopedic knowledge source in many languages
• Conversations: Forum posts, Q&A sites, and social media provide conversational patterns

Raw data requires extensive cleaning and filtering. This includes removing duplicate content, filtering out low-quality or toxic text, balancing the mix of domains and languages, and removing personally identifiable information. Data quality has become recognized as one of the most important factors in LLM performance, often more impactful than model size.

The quality of training data is arguably more important than the size of the model. A smaller model trained on carefully curated data can outperform a larger model trained on noisy, unfiltered web text.

Stage 2: Pre-training

Pre-training is the most expensive and computationally intensive stage. The model learns to predict the next token in a sequence by processing the entire training corpus. This self-supervised learning requires no human-labeled data -- the labels come from the text itself.

The Training Loop

At each step, the model processes a batch of text sequences, predicts the next token at each position, computes the cross-entropy loss between predictions and actual tokens, and updates weights through backpropagation. This loop repeats billions of times.

Compute Requirements

Pre-training frontier LLMs requires staggering amounts of compute:

• Hardware: Thousands of GPUs (NVIDIA H100 or equivalent) connected by high-speed networks
• Time: Weeks to months of continuous training
• Cost: Tens to hundreds of millions of dollars for the largest models
• Energy: Training a single large model can consume megawatt-hours of electricity

Distributed training across thousands of GPUs requires sophisticated parallelism strategies: data parallelism (splitting batches), tensor parallelism (splitting layers), and pipeline parallelism (splitting the model across stages). Frameworks like Megatron-LM, DeepSpeed, and FSDP make this possible.

Stage 3: Supervised Fine-Tuning (SFT)

After pre-training, the model is a powerful but raw text completion engine. It can finish sentences and generate coherent text, but it does not reliably follow instructions or behave like a helpful assistant. Supervised fine-tuning bridges this gap.

In SFT, human annotators write high-quality examples of desired model behavior. These include:

• Question-answer pairs demonstrating accurate, helpful responses
• Multi-turn conversations showing appropriate dialogue behavior
• Task completions showing proper formatting and structure
• Examples of refusing inappropriate requests
• Demonstrations of acknowledging uncertainty rather than confabulating

The model is then fine-tuned on these examples using the same next-token prediction objective. Despite using relatively few examples (thousands to hundreds of thousands, compared to trillions of pre-training tokens), SFT dramatically changes the model's behavior.

Stage 4: Alignment Through RLHF

Reinforcement Learning from Human Feedback (RLHF) further refines the model's outputs to be helpful, harmless, and honest. The process involves two key steps:

Training a Reward Model

Human raters are shown pairs of model outputs for the same prompt and asked which response is better. These preference judgments are used to train a reward model that can predict human preferences automatically.

Policy Optimization

The LLM is then optimized using reinforcement learning (typically PPO or DPO) to generate outputs that the reward model rates highly. A KL divergence penalty prevents the model from deviating too far from the SFT model, avoiding reward hacking.

Some labs now use alternatives to traditional RLHF. Direct Preference Optimization (DPO) skips the reward model entirely, optimizing directly on preference data. Constitutional AI (CAI), used by Anthropic, has the model evaluate its own outputs against a set of principles, reducing reliance on human ratings.

Evaluation and Testing

Before deployment, LLMs undergo extensive evaluation across multiple dimensions:

• Capability benchmarks: MMLU, HumanEval, GSM8K, and others measure knowledge, coding, and reasoning
• Safety testing: Red-teaming exercises probe for harmful outputs, biases, and vulnerabilities
• Alignment testing: Evaluating whether the model follows instructions, acknowledges limitations, and refuses inappropriate requests
• Human evaluation: Blind comparisons where human raters judge output quality, typically reported as ELO-style ratings

The Cost of Building an LLM

The total cost of building a frontier LLM is substantial and growing:

• Data: Millions of dollars for curation, licensing, and annotation
• Compute: Tens to hundreds of millions for pre-training alone
• Human labor: Thousands of annotators for SFT and RLHF data
• Research: Teams of hundreds of researchers and engineers
• Infrastructure: Data centers, networking, and cooling

This cost is why only a handful of organizations can train frontier models from scratch. However, the open-source ecosystem provides alternatives: open-weight models like LLaMA allow organizations to fine-tune and deploy powerful models at a fraction of the cost of training from scratch.

The training of LLMs continues to evolve rapidly. Techniques like synthetic data generation, more efficient training methods, and improved alignment algorithms are constantly being developed. Understanding the training pipeline is essential for anyone working with or building on top of LLMs.