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Integration Examples

See how you can use Burger API's LLM files in real-world scenarios. These examples show what you should prompt and what the AI should ideally return.

Example 1: Creating a New Route​

The Scenario: You want to create a registration endpoint with validation.

User Prompt:

"Using @llms-small.txt, create a route file for user authentication with a POST /register endpoint. Include Zod validation for an email, a password (min 8 chars), and a username."

AI Expected Output: The AI should generate a file similar to this:

import { z } from "zod";
import type { BurgerRequest } from "burger-api";

export const schema = {
  body: z.object({
    email: z.string().email(),
    password: z.string().min(8),
    username: z.string()
  })
};

export const POST = async (req: BurgerRequest) => {
  const { email, password, username } = req.validated.body;
  // Implementation logic here...
  return { status: "success", message: "User registered" };
};

Example 2: Adding Middleware​

The Scenario: You need to implement custom logging middleware.

User Prompt:

"Reference @llms-full.txt to show me how to create a middleware that logs the request method and URL, and then add it to my main app configuration."

AI Expected Output: The AI should provide both the middleware code and where it fits into your configuration:

// middleware/logger.ts
import type { Middleware } from "burger-api";

export const logger: Middleware = async (req) => {
  console.log(`[${new Date().toISOString()}] ${req.method} ${req.url}`);
  return null; // Continue to handler
};

// app.ts
const app = new Burger({
  middleware: [logger]
});

Example 3: CLI Usage​

The Scenario: You are ready to deploy and need to build a single executable.

User Prompt:

"How do I build my Burger API project into a standalone executable? Use @llms-small.txt as reference."

AI Expected Output:

"You can use the Burger API CLI to build an executable. Run the following command: burger-api build:executable This will bundle your application and the runtime into a single binary for easier deployment."

Example 4: Understanding Types​

The Scenario: You are confused about how to access the query parameters.

User Prompt:

"What is the BurgerRequest type and how do I access validated query parameters? Check @llms-full.txt"

AI Expected Output:

"BurgerRequest is the standard request object in Burger API, extending the native Web Request. To access validated query parameters, you first define a schema for query, and then access it via req.validated.query.

Example:

export const schema = {
  query: z.object({ id: z.string() })
};

export const GET = (req: BurgerRequest) => {
  const id = req.validated.query.id;
  // ...
};
```"
info

By referencing the context files, the AI "remembers" the specific naming conventions (like req.validated) that are unique to Burger API.