Building ChatGPT: A Comprehensive Guide to Large Language Models

When you type a question into ChatGPT and press enter, what exactly happens behind that text box? This guide takes you through the entire pipeline of how large language models like ChatGPT are built, from downloading the internet to creating AI assistants.

The Three-Stage Training Process

Building a language model like ChatGPT involves three sequential stages, similar to how we educate children through textbooks, worked examples, and practice problems.

Stage 1: Pre-training - Building Knowledge from the Internet

The first step involves downloading and processing massive amounts of text from the internet. Companies create datasets like Fine Web, which contains about 44 terabytes of filtered internet text - roughly 15 trillion tokens.

Data Collection Process:

• Start with Common Crawl data (2.7 billion web pages as of 2024)
• Apply URL filtering to remove malware, spam, and inappropriate sites
• Extract clean text from HTML markup
• Filter by language (Fine Web keeps pages that are 65%+ English)
• Remove duplicate content and personally identifiable information

The result is a massive collection of high-quality, diverse text documents representing human knowledge across countless topics.

Tokenization: Converting Text to Numbers

Before feeding text into neural networks, we must convert it into tokens - small chunks of text that serve as the basic units of processing. GPT-4 uses about 100,000 different possible tokens in its vocabulary.

For example, “hello world” becomes two tokens: “hello” (ID 15339) and " world" (ID 1917). This tokenization process uses an algorithm called Byte Pair Encoding to balance vocabulary size with sequence length.

Neural Network Training

The pre-training process works by predicting the next token in a sequence. Given a context of tokens, the neural network outputs probabilities for what token should come next. Through millions of updates, the network learns the statistical patterns of how text flows.

This training happens on thousands of GPUs over several months, costing millions of dollars. The result is a “base model” - essentially an internet document simulator that can generate text with similar statistical properties to its training data.

Stage 2: Supervised Fine-Tuning - Learning to Be an Assistant

Base models can generate internet-like text, but they can’t answer questions or follow instructions. To create an assistant, we need the second training stage.

Creating Conversation Datasets

Human labelers create millions of conversations between humans and ideal AI assistants. These labelers follow detailed instructions about being helpful, truthful, and harmless. They write both the human prompts and the perfect assistant responses.

Modern approaches use AI assistance - existing language models help generate responses that humans then edit and refine. This creates massive datasets of high-quality conversations across diverse topics.

Training Process

The base model continues training on this conversation dataset instead of internet documents. The model rapidly learns to imitate the statistical patterns of how ideal assistants respond to human queries.

This stage is much shorter than pre-training - typically hours rather than months - because conversation datasets are much smaller than internet text collections.

Stage 3: Reinforcement Learning - Discovering Better Solutions

The final stage helps models discover their own problem-solving strategies rather than just imitating humans.

The Practice Problem Approach

Like students working practice problems, models generate many different solutions to the same prompt. Solutions that reach correct answers are reinforced, while incorrect solutions are discouraged.

For math problems, this is straightforward - we can automatically check if the final answer is correct. The model tries thousands of different solution paths and learns which approaches work reliably.

Emergent Thinking Strategies

Through this process, models discover cognitive strategies humans use internally but don’t write down. They learn to:

• Double-check their work from different perspectives
• Try multiple approaches to the same problem
• Backtrack when they make errors
• Break complex problems into simpler steps

This creates “thinking models” that show their reasoning process, leading to much higher accuracy on complex problems.

Understanding Model Psychology

These training processes create systems with interesting cognitive properties and limitations.

Hallucinations and Mitigations

Models will confidently make up information when they don’t know the answer, because they’re trained to imitate confident responses. Modern systems mitigate this by:

• Teaching models to say “I don’t know” when uncertain
• Providing web search tools to look up current information
• Using code interpreters for mathematical calculations

Computational Limitations

Models have finite computation per token, so they can’t solve complex problems in a single step. They need to distribute reasoning across many tokens, creating intermediate results they can build upon.

This explains why models struggle with tasks like counting characters in words - they see tokens, not individual letters, and counting requires more computation than fits in a single forward pass.

Knowledge vs. Working Memory

Information stored in model parameters is like vague recollection, while information in the context window is like working memory. For best results, provide relevant information directly in your prompt rather than relying on the model’s memory.

The Current Landscape

Today’s most capable models combine all three training stages. GPT-4 and similar models excel at many tasks but still have limitations. The newest “thinking models” like OpenAI’s o3 and DeepSeek’s R1 show dramatic improvements on reasoning tasks through advanced reinforcement learning.

These systems represent a new frontier in AI capabilities, but they’re still tools that require human oversight. They can hallucinate, make arithmetic errors, or fail on surprisingly simple tasks while succeeding at complex ones.

Looking Forward

The field continues advancing rapidly with multimodal capabilities (handling images, audio, and video), longer-running autonomous agents, and better integration into existing tools. The fundamental training paradigm remains the same, but the scale and sophistication continue growing.

Understanding these systems helps you use them more effectively - as powerful tools that augment human capabilities rather than infallible oracles. Check their work, verify important claims, and use them for inspiration and first drafts while maintaining responsibility for the final output.