How to Scaffold a Python FastAPI Service Using AI Prompt Architect

This guide shows you how to use AI Prompt Architect to generate a production-ready Python FastAPI microservice — with Pydantic validation, async database access, structured logging, and Docker deployment.

The Problem with Unstructured API Development

Starting a FastAPI project without a structured prompt leads to inconsistent patterns across your codebase:

• Mixed sync and async database calls
• Inconsistent error response formats
• No clear separation between routes, services, and data access
• Missing authentication middleware
• No standardised logging or observability

A Master Prompt prevents all of this by encoding your architectural decisions upfront.

Step 1: Configure Your Stack in the Wizard

# FastAPI project configuration
framework: FastAPI 0.115+
language: Python 3.12 (type hints required)
validation: Pydantic v2
database: SQLAlchemy 2.0 (async) + PostgreSQL
auth: JWT with python-jose
testing: pytest + httpx (async)
deployment: Docker + uvicorn

Step 2: Generate the Master Prompt

AI Prompt Architect produces a structured prompt that enforces your full architecture. Here's the folder structure it specifies:

## Folder Structure
app/
├── main.py               # FastAPI application factory
├── config.py             # Pydantic Settings for env vars
├── dependencies.py       # Dependency injection (DB session, auth)
├── models/               # SQLAlchemy ORM models
│   ├── base.py           # Declarative base + mixins
│   └── user.py
├── schemas/              # Pydantic request/response schemas
│   └── user.py
├── routers/              # API route handlers
│   └── users.py
├── services/             # Business logic layer
│   └── user_service.py
├── middleware/            # Custom middleware (CORS, logging, auth)
│   └── auth.py
└── tests/
    ├── conftest.py       # Fixtures: async client, test DB
    └── test_users.py

Step 3: Scaffold with Your AI Assistant

Paste the Master Prompt and ask your AI to generate specific components. The AI follows the exact layered architecture you defined:

# app/routers/users.py — Generated by AI with Master Prompt

from fastapi import APIRouter, Depends, HTTPException, status
from sqlalchemy.ext.asyncio import AsyncSession

from app.dependencies import get_db, get_current_user
from app.schemas.user import UserCreate, UserResponse, UserUpdate
from app.services.user_service import UserService
from app.models.user import User

router = APIRouter(prefix="/users", tags=["users"])

@router.post("/", response_model=UserResponse, status_code=status.HTTP_201_CREATED)
async def create_user(
    payload: UserCreate,
    db: AsyncSession = Depends(get_db),
) -> UserResponse:
    """Create a new user account."""
    service = UserService(db)
    user = await service.create(payload)
    return UserResponse.model_validate(user)

@router.get("/me", response_model=UserResponse)
async def get_current_user_profile(
    current_user: User = Depends(get_current_user),
) -> UserResponse:
    """Return the authenticated user's profile."""
    return UserResponse.model_validate(current_user)

@router.patch("/me", response_model=UserResponse)
async def update_profile(
    payload: UserUpdate,
    current_user: User = Depends(get_current_user),
    db: AsyncSession = Depends(get_db),
) -> UserResponse:
    """Update the authenticated user's profile."""
    service = UserService(db)
    updated = await service.update(current_user.id, payload)
    return UserResponse.model_validate(updated)

Notice how every route follows the same pattern: dependency injection for DB and auth, Pydantic schemas for I/O, service layer for business logic. This consistency comes from the Master Prompt.

Step 4: Generate the Service Layer

The AI generates a clean service layer that separates business logic from HTTP handling:

# app/services/user_service.py

from sqlalchemy import select
from sqlalchemy.ext.asyncio import AsyncSession
from fastapi import HTTPException, status

from app.models.user import User
from app.schemas.user import UserCreate, UserUpdate

class UserService:
    def __init__(self, db: AsyncSession):
        self.db = db

    async def create(self, data: UserCreate) -> User:
        existing = await self.db.execute(
            select(User).where(User.email == data.email)
        )
        if existing.scalar_one_or_none():
            raise HTTPException(
                status_code=status.HTTP_409_CONFLICT,
                detail="Email already registered",
            )

        user = User(**data.model_dump())
        self.db.add(user)
        await self.db.commit()
        await self.db.refresh(user)
        return user

    async def update(self, user_id: str, data: UserUpdate) -> User:
        user = await self.db.get(User, user_id)
        if not user:
            raise HTTPException(status_code=404, detail="User not found")

        for field, value in data.model_dump(exclude_unset=True).items():
            setattr(user, field, value)

        await self.db.commit()
        await self.db.refresh(user)
        return user

Step 5: Refine and Add Infrastructure

Use the Refine feature to add missing infrastructure concerns:

• Structured JSON logging with structlog
• Health check endpoint at /healthz
• Dockerfile with multi-stage build
• Alembic migration configuration
• pytest fixtures with async test client

Key Takeaways

1. Layered architecture from day one — routers, services, and models stay cleanly separated
2. Type safety throughout — Pydantic v2 schemas enforce input/output contracts
3. Async by default — the Master Prompt ensures all DB access is async
4. Production-ready patterns — dependency injection, error handling, and testing are baked in