How AI Models Generate Text (And Why It Matters)
Understanding stochastic sampling, probability, and the magic of one word at a time
You’ve probably heard that tools like ChatGPT “generate language.” But how do they actually do it?
The answer lies in a combination of math, probability, and a process called autoregression. Don’t worry — you don’t need a PhD in computer science to understand it. Let’s unpack the concepts in plain language.
Language Generation is Probabilistic (Not Deterministic)
When you ask ChatGPT a question, it doesn’t search a database and spit out a fixed answer. Instead, it predicts the next word (or word fragment) based on probabilities.
Think of it like this: the model looks at your question and then says,
“Given what I know, what’s the most likely next word?”
But here’s the twist — it doesn’t always choose the most likely word.
Instead, it selects from a probability distribution — a list of possible next words, each with a percentage chance of being picked. That’s what we mean by stochastic generation:
the model rolls a weighted die, not a loaded one.
This is why no two responses are ever exactly alike.
Each Token is Sampled from a Probability Distribution
What’s a token?
In model terms, it’s a chunk of text: a word, part of a word, or even punctuation.
At every step of generating a sentence, the model looks at the probabilities of all possible next tokens, then samples one. For example:
Prompt: “I’m feeling very…”
Model’s next-token options (simplified):
happy (62%)
tired (18%)
anxious (14%)
musical (3%)
rhinoceros (0.5%)
Instead of always picking “happy,” the model might occasionally surprise you with “anxious” or “musical” — making the output more natural, diverse, and less robotic.
It Does This One Token at a Time (Autoregressively)
Here’s the real magic: the model doesn’t generate a sentence all at once.
It goes word by word (or token by token), and each new token is based on all the previous ones. This is called autoregressive generation.
It’s like storytelling as you go:
Step 1: “The lion”
Step 2: “The lion moved”
Step 3: “The lion moved quietly”
Step 4: “The lion moved quietly through”…
…and so on.
Each token is chosen based on everything that came before it. That’s what gives these models their surprisingly coherent flow.
Why This Matters for Professionals
Understanding these mechanics helps you:
Set better expectations. Outputs aren’t fixed or absolute — they’re generated with a touch of randomness.
Prompt more effectively. You can influence the model’s probability distribution by giving clearer, more structured context.
Understand variation. Re-running the same prompt might yield different (but still valid) answers. That’s not a bug — it’s the stochastic process at work.
Final Word
Language models aren’t retrieving answers from a database. They’re predicting — word by word — what might come next based on patterns they’ve seen before. It’s a delicate dance between structure and spontaneity, shaped by the probabilities under the hood.
And just like humans, they think one word ahead at a time.
🧠 Want to go deeper? Next week I’ll unpack how temperature settings influence creativity vs. coherence in model outputs.
Let me know what you’d like decoded next.
— Loren