How Large Language Models Work — From Words to Intelligence

How Large Language Models Work — From Words to Intelligence

Large Language Models (LLMs) are the engines behind ChatGPT, Claude, Gemini, and every modern AI assistant. Understanding how they actually work — not just that they "predict the next word" — is the difference between using AI and mastering it.

What Is an LLM, Really?

At its core, an LLM is a probability machine. Given a sequence of tokens (fragments of text), it predicts which token is most likely to come next. Repeat that process thousands of times and you generate coherent, intelligent-sounding text.

The remarkable insight is this: training a model to predict the next word over trillions of sentences forces it to learn grammar, facts, reasoning patterns, and even common sense — because all of those things are encoded in human-written text.

The Processing Pipeline

When you send a message to an LLM, here is the exact sequence of transformations that occur:

Raw Text → Tokenization → Embedding → Transformer Layers → Logits → Sampling → Output Token

Step 1 — Tokenization: "Hello world" becomes [15496, 995] (numerical IDs). Tokens are sub-word chunks, not full words.

Step 2 — Embedding: Each token ID maps to a high-dimensional vector (e.g., 4096 dimensions). Similar words have similar vectors in this space.

Step 3 — Transformer Layers: The embedded vectors pass through N transformer layers (32–96 layers depending on model size). Each layer refines the representation using attention.

Step 4 — Prediction: The final layer produces a probability distribution over the entire vocabulary (~50,000 tokens). The model samples from this distribution to pick the next token.

Model Size Comparison

The Training Pipeline

LLMs go through multiple training stages:

Stage 1: Pre-training
  - Dataset: Trillions of tokens from the internet, books, code
  - Objective: Next-token prediction (self-supervised)
  - Duration: Weeks to months on thousands of GPUs
  - Result: A base model that completes text

Stage 2: Supervised Fine-Tuning (SFT)
  - Dataset: Human-written instruction-response pairs
  - Objective: Teach the model to follow instructions
  - Result: An instruction-following model

Stage 3: RLHF / DPO
  - Dataset: Human preference rankings between responses
  - Objective: Align the model to human values and safety
  - Result: The helpful, harmless assistant you interact with

Why Next-Token Prediction Leads to Emergent Intelligence

Here is something that surprised even the researchers who built these systems: emergent capabilities — abilities that were never explicitly trained — appear at scale.

Models trained only to predict text spontaneously develop:

• Multi-step arithmetic
• Code debugging
• Logical reasoning
• Language translation
• Summarization

This emergent behavior arises because human text is a compressed representation of human thought, and a sufficiently powerful model learns to decompress it.

Actionable Takeaways

• The "intelligence" of an LLM is learned statistical structure, not symbolic rules
• Context window size determines how much history the model can "remember" during inference
• Pre-trained base models are not assistants — they are text completers; SFT + RLHF creates the helpful assistant layer
• Open-source models (LLaMA 3, Mistral) give you full control; closed models (GPT-4, Claude) offer convenience
• When choosing a model for a project, match parameter count to your hardware budget and task complexity