How to harness AI vibecoding without creating a backend mess
Why This Tutorial?
Vibecoding is trending right now, and I've seen firsthand how AI can dramatically accelerate development. But there's a catch — AI-generated “quick prototypes” morph into weeks of debugging auth issues, inconsistent data models, and leaky abstractions. In this tutorial, I'll show you how I built a secure, scalable movie catalog MVP in under an hour using Cursor (AI-powered code editor) and Adiom’s Data API (a declarative backend service).
Who Am I?
- I have worked with MongoDB and other databases for a long time
- I have very strong backend experience (mainly Go and Java)
- I'm able to read and understand Typescript and JS, and have modest familiarity with modern frameworks like NextJS, but I'm far from being a frontend expert
- Vibecoding skeptic turning advocate: I tested 8+ AI tools (Replit, v0, Claude, Cursor, others) – most create “throwaway” code. Here’s how to avoid that.
The MVP Requirements
Rather than creating a yet another throwaway demo, I wanted to see if I could leverage AI to build something MVP-grade using existing sample data in MongoDB Atlas from the sample_mflix dataset:
- Google OAuth sign-in
- Paginated movie browsing
- User-specific favorite movies
- Secure backend (auth/authz)
- No tightly coupled DB logic in frontend
The Problem With Pure Vibecoding
I was really impressed with agentic workflows in Replit and Cursor. But when I tried to generate the whole application with AI tools alone:
- 20 min: First “working” prototype
- 2 days later: Discovered 3 different data models (one of them was for PostgreSQL somehow?), zero authz, a bunch of dead code, and API endpoints that do inexplicable things 😱
Key lesson: AI excels at code generation, not architectural decisions.
Like one of my friends said, who's a CTO of this cool firm Caylent that specializes in app dev: "as consultants we get brought into vibe coded messes that have repeated data models everywhere and no coherence."
The Secret Sauce: Decoupling Data Access
The key to accelerating development without sacrificing quality lies in properly abstracting the data layer. This is where Adiom's Data API comes in — a declarative backend service that creates a well-defined, secure data access layer almost instantly.
Instead of letting AI directly touch the database, by using this approach, we can:
- Ground the AI code generation with a robust semantic data layer
- Enforce well-structured data access patterns
- Provide type-safe, versioned endpoints
- Implement proper authorization rules
Result: Vibecoding stays within guardrails, avoiding backend chaos.
Preparation: Set up Atlas Cluster
Set up an Atlas Cluster at cloud.mongodb.com and load the sample_mflix dataset. A free tier cluster will work fine for this.
Add 35.247.121.225 (hosted Data API service IP) to Atlas Cluster IP whitelist.
Step 1: Define The Data Model with Protocol Buffers
First, we need to define our data model and API endpoints using Protocol Buffers. They are an industry standard when it comes to low-latency, high-performance data serialization.
Since we're using an existing MongoDB instance, I used Claude with MongoDB's MCP server to help generate the proto file based on schema analysis.
Output: Clean, versioned schema that required some tweaking (final version)
The proto file needs to be compiled with the buf command:
buf build -o protos.pb
Step 2: Create a Config File for Data API
Next, create a YAML configuration file that tells the Data API how to implement these endpoints and what authorization rules to apply (full config file):
Key points in the configuration:
- Single connection to MongoDB Atlas
- Per-endpoint authorization rules
- JWK token validation for security
- Custom queries for accessing and modifying data
Step 3: Deploy the Backend in 3 Clicks
With the proto and configuration files ready, deploy the backend service in the Data API sandbox for MongoDB Atlas. This step requires just a few clicks and no additional coding.
The Data API automatically:
- Creates all the necessary endpoints based on our proto definitions
- Implements the authorization rules
- Connects securely to MongoDB Atlas
- Provides monitoring and observability
Step 4: Vibe-Code the Frontend (Cursor Time!)
I used Cursor with a Magic prompt to generate the frontend. The prompt included:
- Application requirements
- The proto file we created
- Instructions for establishing RPC connections and code generation
The full prompt that I used can be found here.
Cursor generated a working React application that:
- Implements Google Sign-In
- Shows paginated movie listings
- Allows users to mark favorites
- Connects to our Data API backend
I only needed minimal "vibe-debugging" to fix a few minor issues.
Results & Takeaways
In under an hour, we built an MVP-grade movie catalog web app with:
- Proper authentication and authorization
- Clean separation between frontend and backend
- Type-safe API contracts
- Monitoring and observability for backend endpoints
Vibecoding Wisdoms
- AI needs constraints (semantic API specification via protobufs)
- Never let LLMs make authz and data model decisions
Try It Yourself
Find the complete code and instructions in this GitHub repository. All you need is a MongoDB Atlas cluster to get started.
We have plans to release the Data API project under an Open Source license, but in the meantime we are making a free hosted sandbox available for the first 50 users who register at dapi-sandbox.adiom.io.
Questions or want to learn more? Add your comments here or join us on Discord - I'll personally help troubleshoot!
Happy vibecoding!
Top comments (0)