I Built a Production API in 47 Minutes. Here's the One Trick That Made It Possible.

I stared at a simple newsletter form that was destroying my site's performance. The fix seemed obvious: integrate directly with our email provider's API. But that meant spinning up backend infrastructure, learning SendPulse's API, choosing between AWS Lambda, Cloudflare Workers, and a dozen other options... This should take days, right?

It took 47 minutes. Here's how.

The Challenge

When we launched the new landing page for SPEQ, our newsletter subscription form was a performance disaster. The off-the-shelf form from our mailing list provider was bloated with third-party dependencies. Lighthouse analysis confirmed what we suspected: poor scores for performance and best practices.

The solution was clear: throw out the heavy generic form and integrate directly with SendPulse's low-level API. But there was a catch—we needed a secure backend proxy to hide our API keys. This meant spinning up new server infrastructure.

The Standard Approach (What I Didn't Do)

The One Trick That Changed Everything

Step 1: The Architecture Conversation

Since we were already hosting our landing page on Cloudflare, I used them for my initial prompt. I started with a simple question about the architecture I had in mind:

"I want to build a secure API proxy for SendPulse email subscriptions
using Cloudflare Workers. Is this a good approach?"

Claude confirmed the architecture and suggested improvements I hadn't considered: token caching to reduce API calls, proper CORS configuration, and rate limiting strategies.

Step 2: Iterative Refinement

I continued the conversation with questions that uncovered edge cases and best practices:

1. "How should I structure this for multiple API routes?"
   → Claude suggested modular architecture with separate route handlers

2. "What's the best way to handle OAuth token expiration?"
   → Recommended in-memory caching with 5-minute buffer before expiration

3. "Should I use environment variables or Cloudflare secrets?"
   → Explained the security implications and recommended secrets for production

4. "How do I set up automated deployment?"
   → Outlined GitHub Actions workflow with proper secret management

Step 3: The Meta Move

"Based on everything we've discussed, write me a comprehensive prompt
I can give to Claude Code to build this entire system correctly."

This generated a detailed specification that included:

• Complete project structure with file-by-file breakdown
• OAuth 2.0 implementation with token caching logic
• GitHub Actions workflow for automated deployment
• Environment setup for both development and production
• Testing commands and deployment instructions
• Documentation requirements with examples

Step 4: One Prompt to Claude Code

I fed the comprehensive specification to Claude Code and watched as it generated:

workers/
├── src/
│   ├── config/
│   │   └── constants.js        # API URLs and configuration
│   ├── routes/
│   │   └── sendpulse.js        # Email subscription handler
│   ├── utils/
│   │   ├── cors.js             # CORS utilities
│   │   ├── validation.js       # Email validation
│   │   └── sendpulse-auth.js   # OAuth with token caching
│   └── index.js                # Main router
├── .github/
│   └── workflows/
│       └── deploy-workers.yml  # Automated CI/CD
├── package.json
├── wrangler.toml              # Cloudflare config
└── README.md                  # Complete documentation (509 lines!)

The Results

The deployment was smooth. The generated code was production-ready:

• ✅ OAuth 2.0 authentication with automatic token refresh
• ✅ Token caching that reduced API calls by 95%
• ✅ Input validation and email sanitization
• ✅ GitHub Actions pipeline with automated deployment
• ✅ Comprehensive documentation including API reference and troubleshooting. This enabled me to setup the Cloudflare worker and Github actions in minutes, and allowed my co-founder to integrate our front-end with this API seamlessly.
• ✅ Health check endpoint for monitoring

What I Learned Later

Within 24 hours of deploying, I was testing the API with cURL and inspecting the results in our mailing list dashboard. I discovered gaps:

• The API could capture additional context (IP address, country, user agent, referrer) but wasn't
• CORS enforcement was too permissive (allowed all origins)
• Rate limiting was basic (could be more sophisticated)
• No tracking mechanism to identify which signup forms performed best

These additions took another hour to implement. And here's the thing: these feature additions were preventable—they were gaps in the prompt.

Some might argue that I had to use the API first to realize these enhancements were necessary. I disagree. The security requirements are standard for any production service. If Claude had been prompted to consider security and testing best practices upfront, these could have been incorporated from the start. If it had been instructed to review the full SendPulse API documentation, it could have suggested capturing additional parameters.

The Real Velocity Equation

Some developers treat AI like a code vending machine: insert prompt, receive code, iterate until it works. But that's backwards. The velocity doesn't come from how fast AI generates code—it comes from how complete your specification is before any code is written.

This build log proves it: Despite being unfamiliar with both Cloudflare Workers and the SendPulse API, we achieved:

• ✅ Fully deployed microservice
• ✅ Automated CI/CD pipeline
• ✅ OAuth authentication with token caching
• ✅ Comprehensive documentation
• ✅ Zero rework on the core implementation

Total time: 47 minutes from idea to production.

Why We're Building SPEQ

This experience crystallized everything we believe about the future of development. The bottleneck isn't AI's speed—AI is already blazingly fast. The bottleneck is specification quality.

SPEQ will ensure you get it right the first time by:

• Prompting you with edge cases, security and architecture questions (and using safe defaults) before generating code
• Maintaining a library of production-grade specification patterns for common scenarios
• Ensuring the specification is complete before a single line of code is written

The future of development lies in empowering the entire Software Development Lifecycle with AI tools that build the right software as quickly as possible.

Get the Full Prompt

Want to see the exact prompt that generated this service? We've published the complete specification—use it as a template for your own projects.

Stay tuned for more build logs as we continue to refine SPEQ and share our learnings from the front lines of AI-assisted development!