From PRD to Production in Hours

Why this matters:‍

We realized that code is no longer hand-crafted — it’s co-authored with AI. At FlytBase, we used Claude Code and Windsurf IDE to build a production-ready backend system. In just a few hours, we created a multi-tenant authentication system, Swagger docs, and a layered API — all with zero manual development effort. No lines of code written by hand. It was a complete game-changer.

The Problem: Traditional Dev Cycles Are Too Slow for AI Time

Building a backend app from scratch — even something as fundamental as a multi-tenant setup — takes a lot of time:

Writing the PRD and architecture docs typically takes 2-3 days
Setting up the folder structure, controllers, and DB schema can take another 1-2 days
Building endpoints, testing, and error handling takes yet another week

And then there’s iteration, refactoring, and debugging… It all adds up. Too much manual work. Too much wasted time.

The AI-Native Solution: Build the Dev Stack With Agents

We decided to flip the script. Instead of spending time writing code ourselves, we engineered the system and let Claude Code do the heavy lifting.

Step 1

Jam With Claude to Generate PRD & System Design

We used Claude 3.7 Sonnet (Claude Desktop) to co-write the PRD. With Claude’s help, we refined the architecture by setting up workflows, roles, and schemas. Then, we asked Claude to generate layered prompts — one after the other — leading from the PRD to pseudo-code, architecture, and API contracts.

Key Unlock:
Claude generated a layered set of 5-10 prompts, each one building on the last. This approach significantly improved code accuracy and reduced bugs.

Step 2

Build Using Claude Code (CLI) + Windsurf IDE

Instead of writing any code ourselves, we used Claude Code to generate all the code through prompt chains. We employed Windsurf IDE for local iteration and smaller edits. The result? Zero manual coding — just prompt engineering and AI doing all the work.

Features Built:

Multi-tenant backend (NestJS + PostgreSQL + TypeORM)
Auth module (login, logout, refresh token)
Full Swagger API spec (auto-generated)
Token-based RBAC middleware
Project structure with modular services

Step 3

Auto-Test, Auto-Debug, Auto-Fix

We used two powerful prompt chains:

“Build and run the code using Docker Compose. Find the errors and fix them.”
→ Claude identifies runtime errors, applies fixes, and reruns until everything works perfectly.
“Run git commit, separate files into logical commits, create a PR against main.”
→ Claude auto-creates commit messages, PR titles, and summaries.

Bonus:

Claude even suggested Redis caching for performance improvements.
It handled test strategy, error handling, and logging.
Claude understood architectural trade-offs (like doc folder design for multi-model AI).

What We Learned

Prompt layering beats one-shot prompting. Breaking complex tasks into manageable chains made everything more efficient.
Claude Code can build full systems — not just small helper functions.
MCP agents (Claude, Windsurf, GitHub) allow true "code copiloting" — across build, commit, and deployment.
AI-native development = context-first development. Docs, specs, and structure matter more than just typing code.

Tools Used

Claude Code (CLI) – AI-first backend generation
Claude 3.7 Sonnet – Desktop jamming & PRD refinement
Windsurf IDE – Contextual inline prompting
MCPs (GitHub + Browser + Terminal) – AI-based CI/CD and debugging
Docker Compose – Runtime testing + auto-healing
Swagger – API spec generation

What We Learned

With AI, it's possible to go from a blank slate to a working backend in hours.

No hand-coded logic
End-to-end system understanding by AI — architecture, context, and constraints

“The entire NestJS backend, including PRD, tests, Swagger docs, and live endpoints — was built 100% by AI.” — SRV, Director of Engineering, FlytBase