How AI Understands Code

AI coding models like Claude and GPT-4 were trained on billions of lines of code — open-source projects, documentation, tutorials, Stack Overflow answers, blog posts, and more.

They don't "memorize" code. They learn patterns. They understand that a React component has a certain structure, that a Python function follows certain conventions, that an API endpoint needs certain parts.

When you ask for code, the AI isn't copy-pasting from a database. It's generating new code based on patterns it's learned — like how you can write a new sentence even though you learned language from reading other people's sentences.

Here's the single most important thing to understand about AI code generation: the AI generates the next most likely token based on everything that came before it.

"Token" just means a piece of text — roughly a word or part of a word. The AI reads your prompt, the conversation history, and any code you've shared, then predicts what should come next, one token at a time.

This means:

More context = better code. If you tell the AI about your project structure, tech stack, and existing code, it writes code that fits perfectly.

Vague prompts = generic code. If you just say "make a website," you'll get something generic. If you say "make a Next.js landing page with Tailwind CSS, a hero section with gradient text, and a feature grid with icons," you'll get exactly that.

AI models have a context window — the amount of text they can "see" at once. Think of it as the AI's working memory.

Modern models have large context windows (100K–200K tokens), which means they can read and understand entire codebases. This is why tools like Cursor can understand your whole project — they feed your code into the context window so the AI knows what it's working with.

When the context fills up, older parts of the conversation get pushed out. That's why long conversations can sometimes feel like the AI "forgot" what you discussed earlier.

You don't need to understand transformer architectures or attention mechanisms. But these three concepts will make you a dramatically better vibe coder:

1. Be specific. The more context you give, the better the code. Don't say "make a form" — say "make a contact form with email validation, a character counter on the message field, and a success toast notification."

2. Share your codebase. Use tools that can see your entire project. Isolated code snippets lead to inconsistent code.

3. Start fresh when needed. If the AI seems confused after a long conversation, start a new chat. A fresh context window means a clear head.