I’ve been thinking about Cursor as a dataflow app. Cursor reads the codebase and translates it into a message for the LLM by taking the system prompt, relevant code snippet, user prompt and chat history. Then Cursor edits the code as per the LLM response and gives you a normal text response in chat too. The whole thing is just data transfer between user and AI.

The actual dataflow goes like this. First is input collection where Cursor grabs the system prompt, user prompt, chat history, and relevant code snippets. Then there’s context assembly where Cursor converts scattered files, project structure, and user intent into a single LLM-readable message object. Then Cursor makes the LLM call where that object is sent to the model. Then there’s response parsing where Cursor splits the response into code diffs and textual explanation. Then the application layer where Cursor inserts or edits code and shows text to the user. That’s it.

So Cursor is a context router and response applier.

Most modern software value is in data movement, not computation. Cursor does almost zero computation. Cursor just moves intent, context, structure, and results around. The pattern is always the same: User Input → Context Selector → Data Packager → External Intelligence → Response Interpreter → Application Executor → User Output. This is basically every AI app now.

I’ve been studying leaked system prompts of apps lately because they tell you everything about how the product actually works. System prompts tell you what the tool thinks it should do, what it refuses to do, how it uses its limited tokens, and how it structures its outputs.

If Cursor uses your API key and makes requests client-side, then the requests are not that special. Cursor is kind of collating some information from your codebase, inserting some prompt that is gathered from the app, and then sending it to OpenAI or Claude. So it is not really different from some Instagram guy saying, “Hey, I will sell you this prompt pack.” But at least client-side requests decentralize the load and won’t flag up the servers.

So what’s the actual value then? The value is in the codebase indexing that figures out what’s relevant. The diff application logic that can modify your code without breaking it. The UX that makes it feel like the AI understands your project when really Cursor is just feeding the LLM the right context at the right time. Cursor is fundamentally a data router that moves your intent to an LLM and moves the LLM response back into your codebase.

To replicate it for industry :

Know what data to send

Know how to package it

Know what to do with the response