Case Study 2 – Code Generation: Building a Project from Scratch

Background

A while ago I was tasked with building a user-analytics dashboard that displays daily active users, attendance frequency, and related metrics on the front end, plus the back-end APIs to aggregate and serve the data. As a back-end engineer, I was only vaguely familiar with front-end frameworks and the ECharts charting library, so the full-stack requirement became the biggest bottleneck.

Rather than stretch the sprint or pull in another teammate, I decided to let the CoStrict AI pair-programmer shoulder the cross-stack work.

How I Used AI

1. Requirements Analysis & Skeleton

I opened with this prompt:

Please analyze the current project structure and design an attendance-statistics feature for me.  
I need both front-end and back-end implementation.  
Start by reviewing the project layout, then propose a solution.

CoStrict scanned the repo, recognized a Python back-end + plain-HTML front-end stack, and returned a plan:

• Back-end: add a new data-access layer
• Front-end: create a statistics page that uses ECharts for visualization
• DB: add an operation-type column and a timestamp column to the existing user-behavior table

2. Back-End Code

I drilled down:

Generate the back-end code. Expose three endpoints:
1. Daily active users for the last 30 days  
2. Attendance data  
3. Absence data  

CoStrict emitted a complete Controller, Service, models, and DTOs—clean, layered, and ready to compile.

3. Front-End Implementation

For the UI I fed CoStrict a step-by-step spec:

Use Element UI plus ECharts. Include:
1. Date-range picker  
2. Line chart for daily absences  
3. Pie chart for user distribution  
4. Data table with details  

Make it responsive so it works on phones and desktops.

The first cut was functional but visually rough. I iterated:

Switch the color palette to the system theme, add animation,  
stack the charts on small screens, and show loading & error states.

4. Testing & Polish

After the code was generated, I asked CoStrict to write unit and integration tests, then pasted any failing output back into the chat for instant debugging.
In one working day I had a feature that would normally have taken a week—and the sprint stayed green.

Key Takeaways

1. Clear, granular prompts win
   The AI needs explicit, bite-sized instructions. Break the epic into stories, then into tasks.

2. Iterate, don’t chase perfection
   Treat the first generation as scaffolding. Refine in quick loops instead of hoping for a single flawless dump.

3. Code review is still mandatory
   AI is fast, but you still own the architecture, security, and style. Read every line.

4. Cross-stack velocity skyrockets
   A back-end dev can now ship a polished front-end feature without hand-offs, cutting calendar time and team dependencies.

This experience convinced me that AI pair-programming isn’t a replacement—it’s an amplifier. It crushes boilerplate and crosses tech-stack borders so engineers can stay focused on core logic and creative design.