How Large Language Models (LLMs) Actually Generate Text

Source: Dev.to

High-Level Overview

A Large Language Model (LLM) is fundamentally a next‑token prediction system.

Given a sequence of tokens as input, the model:

1. Predicts the most probable next token
2. Appends it to the sequence
3. Repeats the process until the response is complete

That’s it.

What LLMs Do Not Do

LLMs do not:

• Look up words in a dictionary at runtime
• Search the internet by default
• Reason like humans

Instead, they rely entirely on statistical patterns learned during training.

Two Core Components of an LLM

1️⃣ Training Data

LLMs are trained on massive text datasets, including:

• Books
• Articles
• Websites
• Code repositories
• Documentation

During training the model learns statistical relationships between tokens. It does not memorize exact sentences, but learns generalizable language patterns.

Example: After “the sun is”, tokens like shining, bright, or hot are statistically likely. These patterns are encoded into the model’s parameters (weights).

2️⃣ Tokenizer and Vocabulary

Before training begins, every LLM is assigned a tokenizer. The tokenizer:

• Splits text into tokens (sub‑word units)
• Converts tokens into numeric IDs
• Defines a fixed vocabulary (e.g., 20 k–100 k tokens)

Important properties:

• The vocabulary is fixed at training time.
• The model can only generate tokens from this vocabulary.
• Different models use different tokenizers.

Tokens Are Not Words

A token might be:

• A full word
• Part of a word
• Include spaces or punctuation

Example:

"unbelievable"

May be split into:

["un", "believ", "able"]

Consequences:

• Token counts ≠ word counts
• Prompt length matters
• Context limits exist

How a Single Token Is Generated

At each step:

1. The model takes the current token sequence.
2. Produces a probability distribution over all tokens.
3. Selects one token (based on a decoding strategy).
4. Appends it to the sequence

This repeats token by token.

Why Output Feels Like “Reasoning”

LLMs appear to reason because:

• Language itself encodes reasoning patterns.
• The model has seen millions of examples of explanations.
• It predicts tokens that look like reasoning.

Internally, it’s still just predicting the next token.

Mental Model (Remember This)

LLMs generate text one token at a time based on probability, not understanding.
If you keep this in mind, most confusion around LLM behavior disappears.

Why This Matters (Especially for RAG)

In Retrieval‑Augmented Generation (RAG) systems:

• The LLM does not know facts; it only knows patterns.
• Retrieved context steers token prediction.

Good retrieval → better next‑token probabilities.

TL;DR

• LLMs are next‑token predictors.
• They don’t think or search by default.
• Tokenizers define what models can generate.
• Everything happens one token at a time.

Understanding this mental model makes prompt engineering, RAG design, and debugging much easier.