How Large Language Models (LLMs) Work
Summary
Modern artificial intelligence has revolutionized the way we process language. Large Language Models (LLMs) work in a way surprisingly similar to how our brains automatically complete sentences.
When we hear:
“the cat meows and the dog…”
we instinctively think:
“barks.”
This seemingly simple mental leap hides an enormous amount of mathematical and computational machinery. These same principles power systems like ChatGPT, LLaMA, and other state‑of‑the‑art language models.
Let’s break down how this actually works — without magic, just math and engineering.
How Do Large Language Models Work?
At a high level, modern language models follow a pipeline that looks like this:
Text → Tokens → Vectors → Neural Networks → Attention → Prediction
Each step plays a critical role in allowing AI to understand and generate language coherently.
Tokenization: Breaking Language into Basic Units
The first step is tokenization — splitting human language into smaller units called tokens.
Tokens can be:
- Full words
- Sub‑words
- Syllables
- Individual characters
Despite how expressive language feels, it is surprisingly finite:
- Translation systems typically use 40,000–50,000 tokens
- Large models like GPT‑4 can handle vocabularies of up to 256,000 tokens
Example:
The word “satisfaction” might be split into tokens such as:
["sat", "is", "f", "action"]
Each token becomes a fundamental unit the model can process.
Vectorization: Placing Words in Multidimensional Space
Once tokenized, each token is transformed into a vector — a point in a high‑dimensional mathematical space.
In this space:
- Similar words are close together (cat, dog, wolf)
- Conceptual relationships emerge (king − man + woman ≈ queen)
- Linguistic patterns appear (walked / walk ≈ swam / swim)
This step converts language into math. Words become numbers that can be added, compared, and transformed.
This is what allows machines to reason statistically about meaning.
Neural Networks: Discovering Hidden Patterns
With vectors in place, a neural network learns how tokens relate to each other probabilistically.
Training involves:
- Splitting data (≈ 70% training, 30% testing)
- Passing token vectors through multiple layers
- Adjusting millions or billions of parameters
- Minimizing prediction error over time
Conceptual (Simplified) Representation
def neural_network(input_tokens):
x = vectorize(input_tokens)
for layer in hidden_layers:
x = apply_weights(x, layer.weights)
x = activation_function(x)
return output_layer(x)
These ideas have existed since the 1950s — but only recently has computing power made them practical at scale.
What Makes These Models Truly Intelligent?
The breakthrough isn’t just predicting the next word.
It’s understanding context.
That’s where attention comes in.
The Attention Mechanism: Focusing on What Matters
Attention allows models to decide which previous words matter most when predicting the next one.
It works using three components:
- Query — what we’re looking for
- Key — what’s available
- Value — the information to extract
Example:
“the cat meows and the dog…”
The model assigns high attention to “cat” and “meows”, allowing it to infer that “barks” is the most likely continuation.
Attention enables:
- Long‑range dependencies
- Context awareness
- Meaningful sentence completion
This is what unlocked modern transformers.
Temperature and Creativity
LLMs don’t always pick the most likely word.
They use a parameter called temperature:
- Low temperature → predictable, deterministic answers
- High temperature → more randomness, creativity
This controlled randomness is why AI responses can feel natural rather than robotic.
RLHF: Learning to Talk Like Humans
A raw language model is not a conversational assistant.
The final leap came from Reinforcement Learning with Human Feedback (RLHF).
This involved:
- Thousands of humans chatting with models
- Rewarding helpful, safe, and clear answers
- Penalizing bad or incoherent responses
RLHF taught models to:
- Hold conversations
- Know when to stop
- Maintain a consistent tone and personality
This is what turned GPT into ChatGPT.
Why Understanding LLMs Matters
Knowing how LLMs work helps you:
- Write better prompts
- Understand limitations and biases
- Use AI responsibly
- Build the next generation of tools
LLMs represent the cutting edge of generative AI, built on math, statistics, and massive computation — not magic.
Final Thoughts
The next time you interact with an AI assistant, remember:
Behind every fluent answer lies:
- Tokens
- Vectors
- Neural networks
- Attention mechanisms
- Human feedback
All working together at incredible scale.
💬 Which applications of LLMs do you find most fascinating?
Let’s discuss in the comments.
Top comments (1)
Great breakdown 👏
I really liked how you explained token → vector → attention without turning it into “AI magic.” The sentence-completion analogy makes it click instantly for beginners. Also appreciate the emphasis on attention and RLHF — that’s where most explanations stop being clear. Solid primer for anyone trying to actually understand LLMs, not just use them.