ChatGPT for Developers: Complete Coding Guide

Master ChatGPT for programming in 7 days - From basics to advanced techniques with real-world examples.

🎯 What You'll Learn

By the end of this guide, you'll be able to:

• ✅ Write effective prompts for code generation
• ✅ Debug code 3x faster with ChatGPT
• ✅ Learn new frameworks in hours instead of days
• ✅ Refactor legacy code confidently
• ✅ Generate comprehensive tests
• ✅ Optimize your development workflow

Time investment: 30 min/day × 7 days Skill level: Beginner to Advanced Cost: Free (ChatGPT Free) or $20/month (Plus for GPT-4)

---

🎬 Quick Demo (30 seconds)

Video Coming Soon

📹 Demo video in production - Expected release: Week of 2025-11-17

The video will demonstrate:

• Real-time code generation with ChatGPT
• Debugging a React component with AI assistance
• Refactoring legacy code step-by-step
• Test generation workflow

What you'll see in 30 seconds:

• How to structure prompts for better code quality
• Live debugging session: finding and fixing a bug
• Before/after comparison of AI-assisted refactoring
• Productivity gains: manual coding vs. ChatGPT-assisted

Full transcript will be available on video release

---

Day 1: Foundations

Understanding ChatGPT's Strengths & Weaknesses

What ChatGPT Does Well:

• ✅ Explaining code concepts
• ✅ Generating boilerplate
• ✅ Debugging logic errors
• ✅ Writing tests
• ✅ Code translation between languages

What ChatGPT Struggles With:

• ❌ Very new libraries (post-2023)
• ❌ Company-specific code
• ❌ Complex system design
• ❌ Security-critical code (review needed)

Your First Prompt

❌ Bad Prompt:

write a login function

✅ Good Prompt:

Create a secure login function in Python using Flask and SQLAlchemy.

Requirements:
- Hash passwords with bcrypt
- Return JWT token on success
- Handle errors (user not found, wrong password)
- Include input validation
- Add docstring with examples

Use type hints and follow PEP 8.

ChatGPT Output:

from flask import jsonify, request
from werkzeug.security import check_password_hash
from models import User
import jwt
import datetime
from typing import Tuple, Dict
import re

def validate_email(email: str) -> bool:
    """Validate email format using regex."""
    pattern = r'^[\w\.-]+@[\w\.-]+\.\w+$'
    return re.match(pattern, email) is not None

def login_user(email: str, password: str) -> Tuple[Dict, int]:
    """
    Authenticate user and return JWT token.

    Args:
        email: User's email address
        password: Plain text password

    Returns:
        Tuple of (response_dict, http_status_code)

    Example:
        >>> login_user('user@example.com', 'password123')
        ({'token': 'eyJ0eXAiOiJKV1...'}, 200)

    Raises:
        ValueError: If email format is invalid
    """

    # Input validation
    if not email or not password:
        return {'error': 'Email and password are required'}, 400

    if not validate_email(email):
        return {'error': 'Invalid email format'}, 400

    # Find user
    user = User.query.filter_by(email=email).first()

    if not user:
        return {'error': 'Invalid credentials'}, 401

    # Verify password
    if not check_password_hash(user.password_hash, password):
        return {'error': 'Invalid credentials'}, 401

    # Generate JWT token
    token = jwt.encode({
        'user_id': user.id,
        'email': user.email,
        'exp': datetime.datetime.utcnow() + datetime.timedelta(hours=24)
    }, app.config['SECRET_KEY'], algorithm='HS256')

    return {
        'token': token,
        'user': {
            'id': user.id,
            'email': user.email,
            'name': user.name
        }
    }, 200

Key lesson: Specific prompts → Better code

Practice Exercise

Generate a function for your project using the good prompt pattern:

1. Specify language and framework
2. List requirements
3. Request error handling
4. Ask for documentation
5. Specify code style

---

Day 2: Debugging with ChatGPT

Debugging Pattern

Template:

I'm getting this error:
[paste error message]

Here's my code:
[paste relevant code]

Context:
- Language/Framework: [e.g., React 18 + TypeScript]
- What I'm trying to do: [brief explanation]
- What I've tried: [list attempts]

Please explain what's wrong and how to fix it.

Real Example: React Infinite Render

Your Code:

function UserProfile({ userId }) {
  const [user, setUser] = useState(null);

  useEffect(() => {
    fetch(`/api/users/${userId}`)
      .then(res => res.json())
      .then(setUser);
  }); // Bug: missing dependency array

  return <div>{user?.name}</div>;
}

Error:

Warning: Maximum update depth exceeded. This can happen when a component
calls setState inside useEffect without a dependency array.

Prompt to ChatGPT:

I'm getting "Maximum update depth exceeded" error in React.

Code:
[paste code above]

Context:
- React 18 + TypeScript
- Trying to fetch user data on component mount
- Error appears immediately on page load

Please explain and fix.

ChatGPT Response:

The problem is the missing dependency array in useEffect.

Current code:
useEffect(() => {
  fetch(`/api/users/${userId}`)
    .then(res => res.json())
    .then(setUser);
}); // Runs after EVERY render

What happens:
1. Component renders
2. useEffect runs, fetches data
3. setUser() causes re-render
4. useEffect runs again (because no dependency array)
5. Infinite loop!

Fix:
useEffect(() => {
  fetch(`/api/users/${userId}`)
    .then(res => res.json())
    .then(setUser);
}, [userId]); // Only run when userId changes

Complete improved version:
useEffect(() => {
  let cancelled = false;

  const fetchUser = async () => {
    try {
      const res = await fetch(`/api/users/${userId}`);
      if (!res.ok) throw new Error('Failed to fetch user');

      const data = await res.json();
      if (!cancelled) setUser(data);
    } catch (error) {
      console.error('Error fetching user:', error);
      // Handle error state
    }
  };

  fetchUser();

  return () => {
    cancelled = true; // Cleanup to prevent state update on unmounted component
  };
}, [userId]);

Debugging Power: ChatGPT explains WHY (not just how to fix)

Practice Exercise

Debug a real issue from your codebase using the template above.

---

Day 3: Learning New Technologies

Learning Pattern

Instead of reading docs for hours, use ChatGPT as a tutor:

Phase 1: Overview (5 min)

Explain [Technology] in simple terms for an experienced developer.

Include:
- What problem it solves
- Key concepts (5-7 most important)
- When to use it vs alternatives
- 30-second example

Example: React developer learning Vue 3

Phase 2: Hello World (10 min)

Create a simple [App Type] using [Technology].

Requirements:
- [Feature 1]
- [Feature 2]
- [Feature 3]
- Include comments explaining key parts
- Production-ready code quality

Example: Todo app with Vue 3 Composition API

Phase 3: Real-World Patterns (15 min)

Show me best practices for [Specific Task] in [Technology].

Include:
- Modern approach (2024)
- Error handling
- Performance considerations
- Common pitfalls to avoid
- Code example

Example: State management in Vue 3

Real Example: Learning FastAPI

Prompt 1:

Explain FastAPI for an Express.js developer.

Include:
- Key differences from Express
- Main advantages
- When to choose FastAPI
- 30-second example

ChatGPT gives clear comparison:

# FastAPI (Python) vs Express (JavaScript) - Key Differences

1. Type Safety: FastAPI uses Python type hints for automatic validation
2. Auto Documentation: Built-in Swagger UI (no extra setup)
3. Async: Native async/await support
4. Performance: Fast as Node.js (due to Starlette/Uvicorn)

When to use FastAPI:
✅ Python ecosystem (ML, data science)
✅ Need automatic API docs
✅ Strong typing important
❌ Node.js ecosystem required
❌ Team only knows JavaScript

# 30-second example
from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class User(BaseModel):
    name: str
    email: str

@app.post("/users")
async def create_user(user: User):
    # Type validation automatic!
    # API docs automatic!
    return {"id": 123, **user.dict()}

# Run: uvicorn main:app --reload
# Docs: http://localhost:8000/docs (auto-generated!)

Prompt 2:

Create a REST API for blog posts using FastAPI.

Requirements:
- CRUD operations (create, read, update, delete)
- SQLite database with SQLAlchemy
- Pydantic models for validation
- Error handling
- Include all necessary imports

Result: Complete working API in 2 minutes

Practice Exercise

Learn a new framework/library in 30 minutes:

1. Get overview prompt
2. Build hello world prompt
3. Implement real feature prompt

---

Day 4: Code Refactoring

Refactoring Pattern

Refactor this code to improve [specific aspect]:

[paste code]

Requirements:
- [Improvement 1]
- [Improvement 2]
- Keep functionality identical
- Add comments explaining changes

Context:
- [Framework/language details]
- [Performance requirements if any]

Real Example: Cleaning Messy React Component

Original Code:

// 300-line component with everything mixed together
function Dashboard() {
  const [users, setUsers] = useState([]);
  const [products, setProducts] = useState([]);
  const [orders, setOrders] = useState([]);
  const [loading, setLoading] = useState(true);
  const [error, setError] = useState(null);
  // ... 10 more state variables

  useEffect(() => {
    // 100 lines of data fetching logic
    fetch('/api/users').then(...)
    fetch('/api/products').then(...)
    fetch('/api/orders').then(...)
    // ... more fetching
  }, []);

  // 50 lines of event handlers

  return (
    <div>
      {/* 150 lines of JSX */}
    </div>
  );
}

Prompt:

Refactor this React component using best practices:

[paste code]

Requirements:
- Extract custom hooks for data fetching
- Separate into smaller components
- Use React Query for caching
- Add proper TypeScript types
- Improve loading/error states

ChatGPT Output:

// 1. Custom hook for data fetching
import { useQuery } from '@tanstack/react-query';

function useDashboardData() {
  const users = useQuery({
    queryKey: ['users'],
    queryFn: () => fetch('/api/users').then(r => r.json())
  });

  const products = useQuery({
    queryKey: ['products'],
    queryFn: () => fetch('/api/products').then(r => r.json())
  });

  const orders = useQuery({
    queryKey: ['orders'],
    queryFn: () => fetch('/api/orders').then(r => r.json())
  });

  return {
    users: users.data,
    products: products.data,
    orders: orders.data,
    isLoading: users.isLoading || products.isLoading || orders.isLoading,
    error: users.error || products.error || orders.error
  };
}

// 2. Main component (now clean)
export function Dashboard() {
  const { users, products, orders, isLoading, error } = useDashboardData();

  if (isLoading) return <LoadingSpinner />;
  if (error) return <ErrorMessage error={error} />;

  return (
    <DashboardLayout>
      <UsersSection users={users} />
      <ProductsSection products={products} />
      <OrdersSection orders={orders} />
    </DashboardLayout>
  );
}

// 3. Separated components
function UsersSection({ users }: { users: User[] }) {
  return (
    <section>
      <h2>Users</h2>
      <UserList users={users} />
    </section>
  );
}

// ... more components

Before: 300 lines, hard to test, hard to read After: 50-line main component, 5 focused sub-components, easy to test

Practice Exercise

Find a messy file in your codebase and refactor it with ChatGPT.

---

Day 5: Test Generation

Testing Pattern

Generate comprehensive tests for this code:

[paste code]

Requirements:
- Framework: [Jest/Pytest/etc]
- Cover happy path and edge cases
- Include setup/teardown if needed
- Mock external dependencies
- Aim for >90% coverage

Real Example: Testing API Endpoint

Code to Test:

@app.route('/api/discounts', methods=['POST'])
def calculate_discount():
    data = request.get_json()

    price = data.get('price')
    user_tier = data.get('user_tier')
    promo_code = data.get('promo_code')

    if not price or price <= 0:
        return jsonify({'error': 'Invalid price'}), 400

    discount = 0

    # Tier discounts
    if user_tier == 'bronze': discount += 5
    elif user_tier == 'silver': discount += 10
    elif user_tier == 'gold': discount += 15

    # Promo code
    if promo_code == 'SAVE20':
        discount += 20

    final_price = price * (1 - discount / 100)

    return jsonify({
        'original_price': price,
        'discount_percent': discount,
        'final_price': final_price
    })

Prompt:

Generate pytest tests for this Flask endpoint:

[paste code]

Requirements:
- Test all user tiers
- Test with/without promo code
- Test edge cases (negative price, invalid tier, etc.)
- Test promo code stacking with tiers
- Use pytest fixtures for app setup

ChatGPT Output:

import pytest
from flask import Flask
from decimal import Decimal

@pytest.fixture
def app():
    """Create Flask app for testing"""
    app = create_app('testing')
    return app

@pytest.fixture
def client(app):
    """Create test client"""
    return app.test_client()

class TestDiscountCalculation:
    """Test discount calculation endpoint"""

    def test_no_tier_no_promo(self, client):
        """Test base case: no discount"""
        response = client.post('/api/discounts', json={
            'price': 100,
            'user_tier': None,
            'promo_code': None
        })

        assert response.status_code == 200
        data = response.get_json()
        assert data['original_price'] == 100
        assert data['discount_percent'] == 0
        assert data['final_price'] == 100

    @pytest.mark.parametrize('tier,expected_discount', [
        ('bronze', 5),
        ('silver', 10),
        ('gold', 15),
    ])
    def test_tier_discounts(self, client, tier, expected_discount):
        """Test each tier discount level"""
        response = client.post('/api/discounts', json={
            'price': 100,
            'user_tier': tier,
            'promo_code': None
        })

        data = response.get_json()
        assert data['discount_percent'] == expected_discount
        assert data['final_price'] == 100 - expected_discount

    def test_promo_code_alone(self, client):
        """Test promo code without tier"""
        response = client.post('/api/discounts', json={
            'price': 100,
            'user_tier': None,
            'promo_code': 'SAVE20'
        })

        data = response.get_json()
        assert data['discount_percent'] == 20
        assert data['final_price'] == 80

    def test_tier_plus_promo_stacking(self, client):
        """Test that tier and promo discounts stack"""
        response = client.post('/api/discounts', json={
            'price': 100,
            'user_tier': 'gold',  # 15%
            'promo_code': 'SAVE20'  # 20%
        })

        data = response.get_json()
        assert data['discount_percent'] == 35  # 15 + 20
        assert data['final_price'] == 65

    def test_invalid_price_zero(self, client):
        """Test error handling for zero price"""
        response = client.post('/api/discounts', json={
            'price': 0,
            'user_tier': 'gold'
        })

        assert response.status_code == 400
        assert 'error' in response.get_json()

    def test_invalid_price_negative(self, client):
        """Test error handling for negative price"""
        response = client.post('/api/discounts', json={
            'price': -50,
            'user_tier': 'gold'
        })

        assert response.status_code == 400

    def test_missing_price(self, client):
        """Test error when price not provided"""
        response = client.post('/api/discounts', json={
            'user_tier': 'gold'
        })

        assert response.status_code == 400

    def test_invalid_promo_code(self, client):
        """Test that invalid promo codes are ignored"""
        response = client.post('/api/discounts', json={
            'price': 100,
            'user_tier': 'silver',
            'promo_code': 'INVALID'
        })

        data = response.get_json()
        # Should only apply tier discount (10%)
        assert data['discount_percent'] == 10

    def test_large_price(self, client):
        """Test with large price values"""
        response = client.post('/api/discounts', json={
            'price': 999999.99,
            'user_tier': 'gold',
            'promo_code': 'SAVE20'
        })

        assert response.status_code == 200
        data = response.get_json()
        # 35% off 999999.99
        expected = 999999.99 * 0.65
        assert abs(data['final_price'] - expected) < 0.01

Coverage: 95%+ with all edge cases

Practice Exercise

Generate tests for a function you wrote recently.

---

Day 6: Advanced Prompting Techniques

Technique 1: Chain of Thought

Ask ChatGPT to explain its reasoning:

[Your coding question]

Please think step-by-step:
1. Analyze the requirements
2. Consider edge cases
3. Choose the best approach
4. Implement the solution
5. Explain trade-offs

Example:

Design a rate limiting system for an API.

Think step-by-step about:
1. Different rate limiting algorithms
2. Where to store rate limit data
3. Edge cases (distributed systems, clock skew)
4. Implementation approach
5. Trade-offs of each decision

Technique 2: Iterative Refinement

[Initial request]

# After seeing output:
"Great! Now improve it by [specific change]"

# Then:
"Add [feature]"

# Then:
"Optimize for [goal]"

Technique 3: Role-Based Prompting

Act as a senior [role] with 10 years experience.

Review this code and provide:
- What a junior would miss
- Best practices specific to [domain]
- Production readiness checklist

Code:
[paste code]

Example:

Act as a senior DevOps engineer.

Review this Dockerfile and provide:
- Security issues
- Performance optimizations
- Production readiness checklist

Dockerfile:
FROM python:latest
COPY . /app
WORKDIR /app
RUN pip install -r requirements.txt
CMD python app.py

ChatGPT (as DevOps expert):

# Issues found:

1. SECURITY: Using 'latest' tag (unpredictable)
2. SECURITY: Running as root (default)
3. PERFORMANCE: Installing all deps every build (no caching)
4. PERFORMANCE: Large image size (python:latest ~900MB)
5. RELIABILITY: No health check
6. DEBUGGING: No logging configuration

# Improved Dockerfile:

FROM python:3.11-slim as builder  # ✅ Specific version, smaller base

# Create non-root user
RUN useradd -m -u 1000 appuser

# Install dependencies (cached layer)
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy app code
COPY --chown=appuser:appuser . .

# Switch to non-root user
USER appuser

# Health check
HEALTHCHECK --interval=30s --timeout=3s \
  CMD python -c "import requests; requests.get('http://localhost:8000/health')"

# Expose port
EXPOSE 8000

# Run app
CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:8000", "app:app"]

# Production checklist:
# ✅ Specific base image version
# ✅ Non-root user
# ✅ Dependency caching
# ✅ Small image size (~150MB vs 900MB)
# ✅ Health check
# ✅ Production WSGI server (gunicorn)
# ✅ Proper logging (gunicorn logs to stdout)

---

Day 7: Building Your Workflow

Create Your Prompt Library

Save effective prompts for reuse:

# prompts.md

## Code Review
Act as a senior developer. Review this [language] code for:
1. Bugs and logic errors
2. Performance issues
3. Security vulnerabilities
4. Code style and readability
5. Missing edge cases

Provide specific line numbers and fixes.

---

## Documentation
Generate comprehensive documentation for this code:
- Purpose and overview
- Parameters with types
- Return values
- Usage examples
- Edge cases and error handling

Format: [JSDoc/docstring/etc]

---

## Refactoring
Refactor this code following [framework] best practices:
- Improve readability
- Reduce complexity
- Optimize performance
- Add proper types
- Follow [style guide]

Explain each major change.

Keyboard Shortcuts / IDE Integration

VS Code Extension: ChatGPT - Genie AI

• Select code → Right click → "Ask ChatGPT"
• Keyboard shortcut: Ctrl+Shift+G

Command Line:

# Create alias
alias askgpt='function _ask(){ curl -X POST https://api.openai.com/v1/chat/completions -H "Content-Type: application/json" -H "Authorization: Bearer $OPENAI_API_KEY" -d "{\"model\":\"gpt-4\",\"messages\":[{\"role\":\"user\",\"content\":\"$1\"}]}" | jq -r ".choices[0].message.content"; }; _ask'

# Usage
askgpt "Explain this regex: ^[\w\.-]+@[\w\.-]+\.\w+$"

Daily Workflow Integration

Morning:

• Review overnight errors with ChatGPT
• Plan day's tasks (architecture questions)

During Coding:

• Generate boilerplate
• Debug errors as they occur
• Explain unfamiliar code

Before Commit:

• Generate tests
• Write commit message
• Review changes

Code Review:

• Get AI review before human review
• Explain complex changes for PR description

---

💡 Pro Tips

1. Always Verify AI Code

# Don't trust blindly!
# ❌ Bad: Copy-paste without reading
# ✅ Good: Read, understand, test

ai_generated_code = get_from_chatgpt()

# Read it
understand(ai_generated_code)

# Test it
run_tests(ai_generated_code)

# Review for security
check_security(ai_generated_code)

# Then use it

2. Use ChatGPT to Learn, Not Just Get Answers

# ❌ Bad: "Fix my code"
# ✅ Good: "Explain why this doesn't work and teach me the concept"

3. Combine with Documentation

# Best workflow:
1. Read official docs (15 min)
2. Ask ChatGPT for clarification (5 min)
3. Try implementation (20 min)
4. Debug with ChatGPT if needed (10 min)

Total: 50 min to learn new concept

4. Track Cost (GPT-4)

# If using API
import openai

def count_tokens(text):
    # Approximate: 1 token ≈ 4 characters
    return len(text) // 4

prompt = "Your prompt here"
tokens = count_tokens(prompt)
cost = tokens * $0.03 / 1000  # GPT-4 pricing

print(f"Estimated cost: ${cost:.4f}")

---

⚠️ Common Pitfalls

1. Hallucinated Functions

ChatGPT might invent functions that don't exist:

# ChatGPT might suggest:
result = pandas.magic_function(df)  # ❌ Doesn't exist!

# Always verify in docs

2. Outdated Information

Training data cutoff (Jan 2024 for GPT-4):

# ❌ ChatGPT doesn't know about:
- Libraries released after 2024
- Latest API changes
- Recent security vulnerabilities

# ✅ Solution: Provide documentation in prompt

3. Over-Engineering

ChatGPT tends to suggest complex solutions:

# You asked: "Store user preferences"

# ChatGPT suggests: Redis + RabbitMQ + Kafka + Microservices

# Reality: JSON file is fine for your use case

# Always question: "Is this complexity necessary?"

---

📊 Measuring Your Progress

After 7 days, you should be able to:

Task	Before	After	Improvement
Debugging time	30 min/bug	10 min/bug	3x faster
Learning new library	4 hours	1 hour	4x faster
Writing tests	2x code time	0.5x code time	4x faster
Code reviews	45 min	15 min	3x faster

ROI Calculation:

• Time saved: ~2 hours/day
• Cost: Free or $20/month
• Value: Priceless (learn faster, ship faster)

---

🚀 Next Steps

Week 2: Advanced techniques

• Combine ChatGPT with other tools (Copilot, Cursor)
• Build custom GPTs for your stack
• Automate repetitive tasks

Month 2: Team adoption

• Share prompt library with team
• Establish best practices
• Measure team productivity gains

Quarter 1: Master AI-assisted development

• Build entire features with AI assistance
• Teach junior developers using AI
• Contribute to open source faster

---

🔗 Resources

• OpenAI Playground - Test prompts
• ChatGPT - Start coding
• Prompt Engineering Guide - Learn more
• Our Prompt Library - Ready-to-use prompts

---

Last Updated: 2025-11-09 | Difficulty: Beginner to Advanced | Time: 7 days

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