How does an LLM predict the next word in programming?
Large language models (LLMs) often look like they “know” what they’re doing: they write clean functions, follow frameworks, and even fix bugs. What’s really happening is next-word prediction carried to an extreme scale, trained on vast amounts of text that includes a lot of code and technical discussion.
Next-word prediction: simple rule, huge skill
An LLM generates text one token at a time. A token is usually a word part, symbol, or punctuation mark (for code, tokens might be def, (, :, whitespace, or parts of identifiers). Given a prompt, the model estimates a probability distribution over the next token and picks one (often the most likely, sometimes sampled with constraints).
This looks trivial, but the hard part is the probability estimate. During training, the model reads massive corpora and repeatedly learns to answer: “Given the preceding tokens, what token tends to come next?” The training objective pushes it to compress patterns of language, logic, and structure into its parameters. When the same patterns show up at use time, it can continue them.
Why code is especially predictable
Programming languages are designed to be consistent. That makes code more predictable than many forms of prose.
Strong syntax constraints
If a prompt contains for ( in many languages, the next tokens are constrained by grammar. After if condition: in Python, an indented block is expected. The model learns these constraints statistically. It doesn’t “run a parser” in the traditional sense, but the learned distribution heavily favors tokens that keep the code syntactically valid.
Repeated templates and idioms
Real-world code repeats common shapes:
- open file → read → close (or use a context manager)
- validate input → parse → compute → return
- define route/controller → call service → return response
- test setup → act → assert
Because training data contains many variations of these workflows, the model can reproduce them in new contexts. When asked for a “CRUD endpoint” or “binary search,” it often continues with a familiar scaffold.
Local consistency is easy to learn
Coding style has local regularities: indentation, bracket placement, naming patterns, and paired delimiters. Once the model sees a few lines, it can extend the same formatting. That alone can make output feel “professional” even before correctness is considered.
Why it can output long, correct-looking programs
Long code generation works when the model maintains a coherent plan across many steps. Several factors help.
It learns multi-step structure from examples
Training data includes tutorials, library docs, pull requests, code reviews, and full projects. Many samples show complete files: imports at the top, configuration next, then classes, then helpers, then tests. The model learns the typical order and the kinds of statements that appear together, so it can produce a full module that looks like what developers write.
Long-range context keeps it consistent
Modern LLMs can attend to thousands of previous tokens. That means earlier choices (function names, types, endpoints, variables) remain visible while generating later lines. If your prompt defines UserService and earlier code adds create_user, the model is more likely to call that method consistently later.
“Correct” often means “matches common solutions”
Many programming tasks asked in interviews, daily work tickets, or coding assistants have standard solutions. The model may have seen near-identical patterns during training. It’s not retrieving a file verbatim; it’s producing a statistically likely continuation that mirrors common implementations.
Hidden self-check signals
Even without executing code, the model has learned correlations between bugs and surrounding text. For instance, missing a closing bracket tends to make later tokens look wrong in training. The model can avoid some errors because it has learned what “broken code continuations” look like.
Why it still fails in coding jobs
Next-token prediction is powerful, but it doesn’t guarantee truth.
- It may invent APIs that feel plausible.
- It can miss edge cases not mentioned in the prompt.
- It might produce code that compiles but violates business rules.
- Subtle off-by-one issues or concurrency problems can slip through.
In practice, LLMs excel at scaffolding, refactoring, translating between languages, writing tests, and suggesting fixes. They become far more reliable when paired with constraints: explicit requirements, existing code context, type hints, compiler errors, and test results.
What to take away
An LLM predicts the next token, yet that simple objective captures a massive amount of coding structure: grammar, idioms, architecture patterns, and style. Code is predictable, and software work is full of repeated templates, so the model can often write long stretches that look exact and correct. The gap between “looks correct” and “is correct” is where reviews, tests, and execution still matter.
