Building an LLM from Scratch: How Large Language Models Actually Work (2026 Guide)
Published: July 2026 | Reading Time: 12 Minutes
Artificial Intelligence has become part of our daily lives. Whether you're using ChatGPT, Claude, or Gemini, you've probably wondered:
How do these AI models actually work?
Behind every AI chatbot lies a sophisticated pipeline involving trillions of words, billions of parameters, advanced neural networks, and reinforcement learning. In this guide, we'll explain the complete journey of a Large Language Model (LLM) — from collecting raw internet data to becoming an intelligent AI assistant.
What You'll Learn
- What is LLM pretraining?
- How tokenization works (Byte Pair Encoding)
- How AI models learn language
- GPT-2 vs LLaMA 3.1 comparison
- Understanding the Transformer architecture
- Post-training and instruction tuning
- Why AI hallucinates
- Reinforcement Learning (RL)
- Reinforcement Learning from Human Feedback (RLHF)
Phase 1: Pretraining on Massive Data
Before an AI can answer your questions, it must first learn language.
This learning stage is called Pretraining.
Modern LLMs are trained on enormous datasets collected from books, websites, research papers, forums, and other publicly available sources.
Some datasets contain over 15 trillion tokens, requiring more than 40 TB of storage.
However, internet data is messy. Before training starts, the data goes through several cleaning stages.
1. URL Filtering
Unsafe, spam, adult, malicious, or low-quality websites are removed.
2. Text Extraction
HTML pages are converted into plain readable text by removing tags, advertisements, navigation menus, and unnecessary formatting.
3. Language Filtering
Only documents containing sufficient content in the target language are kept. For example, English models keep English pages.
4. Personal Information Removal
Sensitive information like phone numbers, addresses, Social Security Numbers, and email IDs is removed to improve privacy and ethics.
How Tokenization Works
Computers cannot understand words directly — they only understand numbers. Therefore, every sentence is converted into tokens.
A token can be a word, part of a word, a punctuation mark, or even a single character. For example, "Unbelievable" may become: Un + believ + able. Each token receives a unique numerical ID.
Byte Pair Encoding (BPE)
Most modern LLMs use Byte Pair Encoding (BPE). Instead of storing every possible word, BPE learns the most frequently occurring character combinations.
Advantages include:
- Smaller vocabulary
- Faster training
- Better handling of unknown words
- Reduced memory usage
This is why AI sometimes struggles with rare names, unusual spellings, or counting letters accurately.
How Model Training Works
Training is surprisingly simple in concept. The model repeatedly performs one task:
Predict the next token.
For example: "The cat sat on the ____" — the correct answer is "mat."
Initially, the model guesses randomly. If it predicts incorrectly, the error is calculated. Then millions — even billions — of neural network weights are adjusted. This process repeats trillions of times until the model becomes increasingly accurate.
During Inference (When You Chat with AI)
When you ask ChatGPT a question:
- Your prompt is tokenized.
- The model predicts the most probable next token.
- It generates one token at a time.
- Those tokens form sentences.
Importantly, the model does not search a database for answers. Instead, it predicts statistically likely continuations based on patterns learned during training. This explains why AI can occasionally sound convincing while being incorrect.
GPT-2 vs LLaMA 3.1
| Feature | GPT-2 | LLaMA 3.1 |
|---|---|---|
| Parameters | 1.5 Billion | 400 Billion |
| Context Window | 1,024 Tokens | 8,192 Tokens |
| Training Data | 40 GB | ~15 Trillion Tokens |
| Release Year | 2019 | 2024 |
If you want to compare the AI coding assistants built on these models, see our full list of AI coding tools reviewed and ranked for 2026.
Transformer Architecture
Almost every modern LLM is built using the Transformer architecture introduced in the landmark 2017 paper Attention Is All You Need. Unlike older neural networks, Transformers process all words simultaneously using Self-Attention, enabling them to understand long-range relationships efficiently.
Three Types of Transformers
1. Encoder Models
Best for text classification, sentiment analysis, and Named Entity Recognition. Examples: BERT, RoBERTa, DeBERTa.
2. Encoder-Decoder Models
Used for translation, summarization, and caption generation. Examples: T5, BART.
3. Decoder-Only Models
These generate text one token at a time. Examples: GPT, LLaMA, Claude, Gemini, Mistral. These models power modern AI chatbots.
Understanding Self-Attention
Self-Attention allows every word in a sentence to consider every other word when determining its meaning.
For example: "I deposited money at the bank" vs "We sat beside the river bank." Although both contain the word bank, the surrounding words help the model understand the correct meaning. This contextual reasoning is one of the Transformer architecture's greatest strengths.
Post-Training: Turning a Base Model into an AI Assistant
A pretrained model simply predicts text. To make it helpful and conversational, it undergoes Post-Training, which includes:
- Human-written example conversations
- Instruction tuning
- Fine-tuning
- Safety alignment
After this stage, the model learns to answer questions politely, write code, summarize documents, and follow instructions.
Why Do LLMs Hallucinate?
A hallucination occurs when an AI confidently generates information that is false or fabricated. This happens because the model predicts likely text rather than verifying facts.
Researchers are addressing this through better training data, refusal training, fact verification, and tool use (web search, databases, APIs). Modern AI systems increasingly integrate real-time information retrieval to improve factual accuracy.
Reinforcement Learning (RL)
Supervised learning teaches a model by showing correct answers. Reinforcement Learning teaches by trial and error. The model generates multiple answers, evaluates which are best, rewards successful behavior, and repeats the process thousands of times.
DeepSeek R1: Reinforcement Learning in Action
DeepSeek R1 demonstrated that Reinforcement Learning can produce impressive reasoning abilities. As training progressed, the model naturally began thinking longer, exploring multiple solutions, revising earlier assumptions, and producing more accurate answers. Interestingly, these reasoning behaviors were not explicitly programmed — they emerged through optimization.
Reinforcement Learning from Human Feedback (RLHF)
Many tasks don't have one correct answer. For example: write a poem, recommend a vacation, or draft an email using an AI writing tool. RLHF solves this challenge.
Here's how it works:
- Humans rank multiple AI responses.
- A reward model learns those preferences.
- Reinforcement Learning optimizes the AI to produce responses humans prefer.
This process significantly improves helpfulness, tone, and conversational quality.
Advantages of RLHF
- Better alignment with human expectations
- More helpful responses
- Improved conversational quality
- Safer AI behavior
Limitations
- Human bias can influence the model
- Collecting feedback is expensive
- Models may optimize for reward scores rather than true usefulness
The Complete LLM Pipeline
Massive Data Collection → Data Cleaning → Tokenization → Pretraining → Transformer Learning → Instruction Tuning → Reinforcement Learning → RLHF → AI Assistant
Each stage builds upon the previous one, transforming raw internet text into the intelligent assistants millions of people use every day.
Final Thoughts
Large Language Models are among the most significant breakthroughs in artificial intelligence. While they may appear to "understand" language, they fundamentally learn statistical patterns from enormous datasets and generate text one token at a time.
Advances in Transformer architectures, reinforcement learning, and human feedback have made today's AI systems remarkably capable. If you want to explore, compare, and find the right AI tool for your needs, AsmiAI reviews and ranks 230+ AI tools across every category — from coding assistants to writing tools to image generators.
Tags: AI, Artificial Intelligence, Machine Learning, ChatGPT, LLM, NLP, Deep Learning, Generative AI, Transformers, Technology
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