How I Started This Project Using Cursor

As a developer constantly looking for efficient ways to speed up development, I recently started using Cursor, an AI-powered coding assistant, to help me streamline my backend projects. I wanted to build a CRUD API for food profiles using FastAPI while following clean architecture principles. Additionally, I needed Docker support to ensure smooth deployment and testing.

To track the development process, I recorded key timestamps and prompts, which I’ll outline below.

Cursor IDE Intro

Development Steps

🕒 Start Time: 4:05 PM

📌 Bitbucket Repository: nutri-sim-be

Step 1: FastAPI CRUD Boilerplate with Clean Architecture

Prompt:
Create a CRUD FastAPI boilerplate using clean architecture for food profile.

Cursor quickly generated a well-structured FastAPI boilerplate, separating concerns with:
✅ Routers for API endpoints
✅ Services for business logic
✅ Repositories for database interactions
✅ Schemas using Pydantic models

FastAPI CRUD Boilerplate

Step 2: Dockerizing the Application

Prompt:
Create Dockerfile and docker-compose file to make startup, testing, and deployment easier.

To ensure consistency across environments, I needed Docker support. Cursor generated a Dockerfile and docker-compose.yaml, allowing me to:

  • Build a lightweight FastAPI container
  • Set up a PostgreSQL database service
  • Easily spin up the entire stack with docker-compose up

Docker Setup

Step 3: Fixing Postgres Version Incompatibility

Prompt:
Can you fix the following errors? [Postgres version incompatibility error]

I ran into a Postgres version mismatch between my local environment and the Docker container. Cursor helped me adjust the PostgreSQL version in docker-compose.yaml and modify environment variables to ensure compatibility.

Postgres Error Fix

Step 4: Debugging Internal Server Error (Database Issue)

Prompt:
What does this error mean, can you fix it? [Internal server error, database issue]

After running migrations, I encountered an Internal Server Error due to misconfigured database settings. Cursor identified the issue, suggesting:

  • Updating SQLAlchemy connection strings
  • Adjusting Alembic migration settings

After applying the fixes, database connections worked smoothly. 🚀

Database Error Fix

Step 5: Creating a Postman Collection

Prompt:
Create a Postman collection to test all endpoints.

To simplify API testing, Cursor generated a Postman collection with:

  • CRUD requests for the food profile endpoints
  • Environment variables for API base URL
  • Pre-configured authentication headers

This made testing much faster! ✅

Postman Collection

Step 6: Fixing Type Errors in Food Profile Creation

Prompt:
Can you fix this type error? [TypeError upon creating food]

Upon testing, I encountered a TypeError due to incorrect data validation. Cursor helped:

  • Adjust Pydantic models to properly parse input types
  • Add request validation handlers to return clearer error messages

Once fixed, the API accepted and stored food profiles correctly. 🥗

Type Error Fix

Final Outcome

🕒 End Time: 4:47 PM
Total Duration: 42 minutes ⏳

In just under an hour, I successfully: ✅ Built a FastAPI CRUD application with clean architecture
✅ Dockerized the setup for easy deployment
✅ Resolved database compatibility issues
✅ Fixed server errors and type validation bugs
✅ Created a Postman collection for streamlined testing

Conclusion

Using Cursor significantly accelerated my development process, allowing me to build and debug a fully functional FastAPI backend in record time. Instead of manually fixing errors and writing boilerplate code, I could focus on refining the architecture and improving performance.

Read More:
AI Model: Introduction