AI Development Prompts and Their Responses: A Practical Guide 2024-2025
Introduction
Understanding how AI responds to development prompts is crucial for getting the best results. Let's explore real examples of prompts and their corresponding responses.
1. Project Structure Response
Prompt:
"Create a modern project folder structure for a full-stack application using:
- Frontend: Next.js with TypeScript
- Backend: FastAPI
- Database: PostgreSQL
Include all necessary directories and explain their purpose."
AI Response:
I'll help you create a scalable project structure for your full-stack application.
Here's the recommended structure:
project-root/
├── frontend/
│ ├── src/
│ │ ├── components/
│ │ │ ├── common/ # Reusable UI components
│ │ │ ├── features/ # Feature-specific components
│ │ │ └── layouts/ # Page layouts and templates
│ │ ├── hooks/ # Custom React hooks
│ │ ├── services/ # API integration and services
│ │ ├── utils/ # Helper functions and utilities
│ │ ├── types/ # TypeScript type definitions
│ │ └── pages/ # Next.js pages and routing
│ ├── public/ # Static assets
│ └── tests/ # Frontend tests
├── backend/
│ ├── app/
│ │ ├── api/ # API endpoints
│ │ ├── core/ # Core functionality
│ │ ├── models/ # Database models
│ │ ├── services/ # Business logic
│ │ └── utils/ # Helper functions
│ ├── tests/ # Backend tests
│ └── alembic/ # Database migrations
└── docker/ # Docker configuration
Let me explain the key directories...
[Detailed explanation follows]
2. Component Creation Example
Prompt:
"Create a reusable React component for a dashboard card that:
- Accepts title, value, and trend as props
- Shows a trend indicator (up/down arrow)
- Uses Tailwind CSS for styling
- Includes TypeScript types
Please include comments explaining the code."
AI Response:
// DashboardCard.tsx
// A reusable card component for displaying metrics with trends
import React from 'react';
import { ArrowUp, ArrowDown } from 'lucide-react';
interface DashboardCardProps {
title: string;
value: number | string;
trend: number;
subtitle?: string;
}
export const DashboardCard: React.FC<DashboardCardProps> = ({
title,
value,
trend,
subtitle
}) => {
// Determine if trend is positive
const isPositive = trend > 0;
return (
<div className="p-6 rounded-lg bg-white shadow-sm hover:shadow-md transition-shadow">
<div className="flex justify-between items-start">
<h3 className="text-gray-600 font-medium">{title}</h3>
<div className={`flex items-center ${
isPositive ? 'text-green-500' : 'text-red-500'
}`}>
{isPositive ? <ArrowUp size={20} /> : <ArrowDown size={20} />}
<span className="ml-1">{Math.abs(trend)}%</span>
</div>
</div>
<div className="mt-4">
<span className="text-2xl font-bold">{value}</span>
{subtitle && (
<p className="text-gray-500 text-sm mt-1">{subtitle}</p>
)}
</div>
</div>
);
};
// Usage example:
// <DashboardCard
// title="Monthly Revenue"
// value="$50,000"
// trend={12.5}
// subtitle="Last 30 days"
// />
3. API Endpoint Implementation
Prompt:
"Generate FastAPI endpoints for user authentication with:
- Login
- Registration
- Password reset
Include request models, validation, and error handling."
AI Response:
# auth_router.py
from fastapi import APIRouter, HTTPException, Depends
from pydantic import BaseModel, EmailStr
from typing import Optional
from .services import AuthService
from .models import UserCreate, UserResponse
router = APIRouter(prefix="/auth", tags=["authentication"])
class LoginRequest(BaseModel):
email: EmailStr
password: str
class LoginResponse(BaseModel):
access_token: str
token_type: str
user: UserResponse
@router.post("/login", response_model=LoginResponse)
async def login(credentials: LoginRequest):
try:
# Authenticate user
user = await AuthService.authenticate_user(
credentials.email,
credentials.password
)
# Generate token
token = AuthService.create_access_token(user.id)
return {
"access_token": token,
"token_type": "bearer",
"user": user
}
except ValueError as e:
raise HTTPException(
status_code=401,
detail="Invalid credentials"
)
@router.post("/register", response_model=UserResponse)
async def register(user_data: UserCreate):
try:
user = await AuthService.create_user(user_data)
return user
except ValueError as e:
raise HTTPException(
status_code=400,
detail=str(e)
)
# [Additional endpoint implementations...]
4. Database Model Creation
Prompt:
"Create SQLAlchemy models for a blog system with:
- Users
- Posts
- Categories
- Comments
Include relationships and indexes."
AI Response:
# models.py
from sqlalchemy import Column, Integer, String, Text, ForeignKey, DateTime
from sqlalchemy.orm import relationship
from sqlalchemy.sql import func
from .database import Base
class User(Base):
__tablename__ = "users"
id = Column(Integer, primary_key=True, index=True)
email = Column(String, unique=True, index=True)
hashed_password = Column(String)
full_name = Column(String)
created_at = Column(DateTime(timezone=True), server_default=func.now())
# Relationships
posts = relationship("Post", back_populates="author")
comments = relationship("Comment", back_populates="author")
class Post(Base):
__tablename__ = "posts"
id = Column(Integer, primary_key=True, index=True)
title = Column(String, index=True)
content = Column(Text)
author_id = Column(Integer, ForeignKey("users.id"))
category_id = Column(Integer, ForeignKey("categories.id"))
created_at = Column(DateTime(timezone=True), server_default=func.now())
updated_at = Column(DateTime(timezone=True), onupdate=func.now())
# Relationships
author = relationship("User", back_populates="posts")
category = relationship("Category", back_populates="posts")
comments = relationship("Comment", back_populates="post")
# [Additional model definitions...]
5. Frontend Service Integration
Prompt:
"Create a React Query hook for managing user data that:
- Fetches user profile
- Updates user information
- Handles loading and error states
Include TypeScript types and error handling."
AI Response:
// hooks/useUser.ts
import { useQuery, useMutation, useQueryClient } from '@tanstack/react-query';
import { axios } from '../lib/axios';
interface User {
id: number;
email: string;
fullName: string;
avatar?: string;
}
interface UpdateUserData {
fullName?: string;
avatar?: string;
}
export const useUser = (userId: number) => {
const queryClient = useQueryClient();
// Fetch user profile
const {
data: user,
isLoading,
error
} = useQuery<User>(['user', userId],
async () => {
const { data } = await axios.get(`/api/users/${userId}`);
return data;
}
);
// Update user profile
const updateUser = useMutation<User, Error, UpdateUserData>(
async (updateData) => {
const { data } = await axios.patch(
`/api/users/${userId}`,
updateData
);
return data;
},
{
// Update cache on success
onSuccess: (updatedUser) => {
queryClient.setQueryData(['user', userId], updatedUser);
},
}
);
return {
user,
isLoading,
error,
updateUser: updateUser.mutate,
isUpdating: updateUser.isLoading,
updateError: updateUser.error
};
};
// Usage example:
// const { user, isLoading, updateUser } = useUser(1);
6. Testing Implementation
Prompt:
"Create tests for a user profile component that:
- Tests rendering
- Handles form submission
- Tests error states
Use React Testing Library and Jest."
AI Response:
// UserProfile.test.tsx
import { render, screen, fireEvent, waitFor } from '@testing-library/react';
import { QueryClient, QueryClientProvider } from '@tanstack/react-query';
import { UserProfile } from './UserProfile';
import { updateUserProfile } from '../api/users';
// Mock API calls
jest.mock('../api/users');
describe('UserProfile', () => {
const queryClient = new QueryClient();
const renderComponent = () => {
return render(
<QueryClientProvider client={queryClient}>
<UserProfile userId={1} />
</QueryClientProvider>
);
};
beforeEach(() => {
jest.clearAllMocks();
});
it('renders user profile form', () => {
renderComponent();
expect(screen.getByLabelText(/full name/i)).toBeInTheDocument();
expect(screen.getByLabelText(/email/i)).toBeInTheDocument();
expect(screen.getByRole('button', { name: /save/i })).toBeInTheDocument();
});
it('handles successful form submission', async () => {
const mockUpdate = updateUserProfile as jest.Mock;
mockUpdate.mockResolvedValueOnce({ success: true });
renderComponent();
fireEvent.change(screen.getByLabelText(/full name/i), {
target: { value: 'John Doe' }
});
fireEvent.click(screen.getByRole('button', { name: /save/i }));
await waitFor(() => {
expect(mockUpdate).toHaveBeenCalledWith({
fullName: 'John Doe'
});
});
expect(screen.getByText(/profile updated/i)).toBeInTheDocument();
});
// [Additional test cases...]
});
Best Practices for Understanding AI Responses
"Could you explain the error handling implementation in more detail and add:
- More specific error types
- Error recovery strategies
- User feedback handling"
Common Response Patterns
Conclusion
When working with AI responses:
Remember that AI responses are starting points that often need refinement for production use.
What patterns have you noticed in AI coding responses? Share your experiences in the comments!
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