Your team has 47 API endpoints that need TypeScript types generated from your OpenAPI spec. Someone has to write 2,000 lines of boilerplate.
Or you describe the pattern once, feed it the spec, and get type-safe code in minutes.
That's not the end. Now you need tests for each endpoint, documentation, and migration scripts.
Code generation isn't about replacing developers. It's about eliminating the gap between knowing what code should exist and having it exist.
CODE AI PRIMITIVE - Transforms natural language or structured input into working code. Essential for automation scaffolding, code migration, and development acceleration.
Code generation takes a prompt - either natural language or structured specification - and produces working code. You describe what the function should do, what inputs it takes, what it returns, and the model writes the implementation. Not pseudo-code. Not suggestions. Actual, runnable code.
Modern language models have internalized patterns from billions of lines of code. They understand not just syntax, but idioms, best practices, and common patterns across languages and frameworks. They can translate between languages, refactor existing code, and generate tests that exercise edge cases.
The real power isn't writing one function. It's generating entire systems: API clients from specs, database schemas from descriptions, test suites from implementations. Code generation turns specifications into artifacts at the speed of thought.
Code generation solves a universal problem: how do you bridge the gap between specification and implementation without tedious manual translation?
Describe what the code should do, provide examples of the desired style and patterns, specify the target language and framework, generate, then validate the output compiles and passes tests before shipping.
Select a task type, language, and detail level. See how the generated code changes. Production code has more lines but handles more edge cases.
async function getUser(id: string): Promise<User> {
const response = await fetch(`/api/users/${id}`);
if (!response.ok) {
throw new ApiError(response.status, await response.text());
}
return response.json();
}Generate from structured specifications
Feed an OpenAPI spec, GraphQL schema, or database schema. The model generates typed code that matches the specification exactly: API clients, server handlers, data models, validation logic. The spec is the source of truth.
Generate from natural language
Describe what you want in plain English: 'Write a function that validates email addresses and returns detailed error messages.' The model infers the implementation, handling edge cases and following language conventions.
Transform existing code
Provide existing code and describe the transformation: 'Convert this JavaScript to TypeScript with strict types' or 'Refactor this class to use dependency injection.' The model preserves behavior while applying the change.
Your backend team updates the OpenAPI spec. Within minutes, the frontend team has a new TypeScript client with full type safety, error handling, and documentation. automatically generated and tested.
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You generate 50 functions and ship them. Three days later, production crashes. The generated code had a subtle bug that compiled fine but failed at runtime. Generated code is not trusted code.
Instead: Always run generated code through your test suite. Generate tests alongside implementations. Never ship unvalidated generated code to production.
You try to generate an entire application in one prompt. The model runs out of context halfway through, loses track of earlier decisions, and produces inconsistent code with conflicting function signatures.
Instead: Generate in focused chunks: one file, one function, one module at a time. Feed back the generated code as context for dependent generations.
You ask for 'a function to sort users' and get a working implementation. But it uses a library your codebase doesn't have, follows a different naming convention, and ignores your error handling patterns.
Instead: Include constraints in your prompt: target language version, available libraries, naming conventions, error handling patterns. Show examples from your codebase.
You've learned how AI generates code from descriptions and specifications. The natural next step is understanding how AI generates audio and video content.