Chapter 7: Building a Full-Stack App with Flask - BlackArmorAI LMS: CS with Python & System Design Thinking Development

Overview

ChatGPT has revolutionized how developers approach coding, design, and problem-solving. This chapter explores how to effectively integrate ChatGPT into your Python development workflow, from initial design and planning to debugging and optimization. You'll learn how to craft effective prompts, validate AI-generated code, and use ChatGPT as a collaborative development partner.

We'll cover practical techniques for using ChatGPT in system design, database modeling, API development, and testing. You'll discover how to leverage AI assistance while maintaining code quality and understanding the underlying principles. These skills will significantly accelerate your development process and improve your problem-solving capabilities.

What You'll Learn:

Effective prompt engineering for development tasks
System design and architecture planning with AI
Database modeling and optimization strategies
Code generation and review techniques
Debugging and troubleshooting with AI assistance
Best practices for AI-assisted development

ChatGPT Development Workflow

ChatGPT Development Use Cases & Prompts

Development Phase	Use Case	Prompt Strategy	Expected Output	Validation Method
Planning	System architecture design	Context + requirements + constraints	Architecture diagram, component list	Review with team, feasibility check
Design	Database schema design	Data requirements + relationships + scale	ERD, table definitions, indexes	Normalization check, performance review
Implementation	Code generation	Function spec + language + patterns	Working code with comments	Unit tests, code review
Testing	Test case generation	Function spec + edge cases + scenarios	Test cases, mock data	Test execution, coverage analysis
Debugging	Error analysis	Error message + context + code	Root cause, solution steps	Solution implementation, verification

AI-Assisted Development Process

graph TD A[Define Problem] --> B[Craft Prompt] B --> C[Generate Response] C --> D[Review Output] D --> E{Quality Check} E -->|Pass| F[Implement Solution] E -->|Fail| G[Refine Prompt] G --> C F --> H[Test Implementation] H --> I{Test Results} I -->|Pass| J[Document Solution] I -->|Fail| K[Debug Issues] K --> L[Ask ChatGPT for Help] L --> M[Get Debugging Tips] M --> N[Apply Fixes] N --> H J --> O[Iterate & Improve] style A fill:#e1f5fe style B fill:#f3e5f5 style C fill:#e8f5e8 style D fill:#fff3e0 style E fill:#fce4ec style F fill:#e0f2f1 style H fill:#f1f8e9 style I fill:#e8eaf6 style J fill:#fafafa style K fill:#fff8e1 style L fill:#f3e5f5 style M fill:#e8f5e8 style N fill:#c8e6c9 style O fill:#e1f5fe

Key ChatGPT Configuration & Prompt Variables

OPENAI_API_KEY

sk-...your-api-key...

Authentication key for OpenAI API access. Required for programmatic ChatGPT integration and custom applications.

OpenAI API Authentication →

CHATGPT_MODEL

gpt-4, gpt-3.5-turbo

Specifies which ChatGPT model to use. Different models have varying capabilities and cost structures.

OpenAI Models Documentation →

MAX_TOKENS

4000, 8000, 32000

Maximum number of tokens in the response. Controls response length and API cost.

Token Limits Documentation →

TEMPERATURE

0.0 - 2.0

Controls randomness in responses. Lower values are more deterministic, higher values more creative.

Temperature Parameter →

SYSTEM_PROMPT

You are a Python expert...

Defines the AI's role and behavior. Sets context and constraints for all interactions.

Chat Completions API →

CONTEXT_WINDOW

4096, 8192, 32768

Maximum tokens for input context. Determines how much information can be included in prompts.

GPT-4 Context Window →

Critical Configurations

AI Tools

ChatGPT Plus (recommended)
Claude or other AI assistants
GitHub Copilot (optional)

Prompting Framework

Context provision
Clear requirements
Output format specification
Iterative refinement

Integration Points

System design phase
Database modeling
Code generation
Debugging sessions

Effective Prompting Techniques

The Five-Step Prompting Framework

Effective prompting is the key to getting useful responses from ChatGPT. Here's a proven framework:

Example: Task Management System Prompt

I am building a task-tracking web app in Flask for small teams.

Requirements:
- Python 3.12, Flask, SQLAlchemy
- SQLite database for development
- CRUD operations for tasks
- Authentication with Flask-Login
- Routes for creating, viewing, updating, and deleting tasks
- Each task should have: title, description, status, assigned user, due date

Generate the SQLAlchemy models and Flask routes, and explain your design choices.

This prompt provides context, specific requirements, technology constraints, and asks for explanation of design decisions.

Prompt Structure Best Practices

Components of a Good Prompt

Context: What you're building and why
Requirements: Specific features and constraints
Technology Stack: Languages, frameworks, databases
Output Format: How you want the response structured
Next Steps: What you'll do with the response

System Architecture Design

Using ChatGPT for Architecture Decisions

ChatGPT can help you design system architecture by considering scalability, maintainability, and performance requirements.

Architecture Design Prompt

I need to design a scalable e-commerce system that can handle:
- 10,000 concurrent users
- Product catalog with 100,000+ items
- Real-time inventory management
- Payment processing
- Order tracking

Technology constraints: Python, Flask, PostgreSQL
Budget: Moderate (can use cloud services)

Design the system architecture with:
1. Component diagram
2. Database schema
3. API endpoints
4. Scalability considerations
5. Security measures

Architecture Patterns

Layered Architecture

Presentation Layer
Application Layer
Data Layer
Infrastructure Layer

Microservices

Service decomposition
API gateways
Service discovery
Data consistency

ChatGPT-Assisted Development Workflow

AI Development Process Dataset

Development Phase	ChatGPT Role	Input Type	Output Type	Success Rate	Time Saved
System Design	Architecture Planning	Requirements Doc	Architecture Diagram	85%	60%
Database Design	Schema Generation	Entity List	ERD + SQLAlchemy Models	90%	70%
Code Generation	Boilerplate Creation	API Spec	Flask Routes + Models	80%	50%
Debugging	Error Analysis	Error Stack Trace	Solution + Explanation	75%	40%
Testing	Test Case Generation	Function Code	Unit Tests	70%	45%
Documentation	Doc Generation	Code Comments	API Documentation	95%	80%

AI-Assisted Development Process Flow

flowchart TD A[Project Requirements] --> B[ChatGPT Analysis] B --> C[System Architecture Design] C --> D[Database Schema Design] D --> E[API Endpoint Planning] E --> F[Code Generation Phase] F --> G[Flask App Structure] F --> H[SQLAlchemy Models] F --> I[Authentication System] F --> J[CRUD Operations] G --> K[Implementation Review] H --> K I --> K J --> K K --> L[Code Testing] L --> M{Code Working?} M -->|No| N[Debug with ChatGPT] N --> O[Error Analysis] O --> P[Solution Generation] P --> Q[Code Fixes] Q --> L M -->|Yes| R[Performance Optimization] R --> S[Documentation Generation] S --> T[Deployment Planning] T --> U[Project Complete] style A fill:#e3f2fd style U fill:#c8e6c9 style M fill:#ffecb3 style N fill:#ffcdd2

ChatGPT Integration Variables & Configuration

API Configuration

OPENAI_API_KEY = "sk-..."

OpenAI API key for programmatic access

📖 OpenAI API Reference

Model Selection

model = "gpt-4"

AI model for code generation and analysis

📖 OpenAI Models

Temperature Setting

temperature = 0.3

Controls creativity vs consistency in responses

📖 Temperature Parameter

Max Tokens

max_tokens = 4000

Maximum response length for code generation

📖 Max Tokens Parameter

Prompt Template

PROMPT_TEMPLATE = "Context: {context}\nRequirements: {requirements}\nOutput: {format}"

Structured prompt template for consistent results

📖 Prompt Engineering Guide

Response Validation

VALIDATION_RULES = ["syntax_check", "security_scan", "performance_review"]

Validation rules for generated code quality

📖 Safety Best Practices

Database Design with AI

Entity-Relationship Design

ChatGPT can help you design database schemas by understanding your domain model and suggesting optimal relationships.

Database Design Prompt

Design a database schema for a project management system with:

Entities:
- Users (employees, managers)
- Projects (with budgets, timelines)
- Tasks (assigned to users, part of projects)
- Time entries (users log time on tasks)
- Comments (on tasks and projects)

Requirements:
- Support for project hierarchies
- Time tracking and reporting
- User role management
- Audit trail for changes

Generate:
1. ERD with relationships
2. SQLAlchemy models
3. Indexing strategy
4. Sample queries for common operations

Normalization and Optimization

Database Design Principles

Normalize to 3NF for data integrity
Denormalize for performance when needed
Use appropriate indexes
Consider partitioning for large tables
Plan for future growth

Code Generation and Boilerplate

Generating Starter Code

ChatGPT excels at generating boilerplate code and starter templates for common patterns.

Flask Application Structure

# Generated by ChatGPT
from flask import Flask, jsonify, request
from flask_sqlalchemy import SQLAlchemy
from flask_login import LoginManager, UserMixin, login_user, logout_user, login_required
from werkzeug.security import generate_password_hash, check_password_hash
import os

app = Flask(__name__)
app.config['SECRET_KEY'] = os.environ.get('SECRET_KEY', 'dev-key-change-in-production')
app.config['SQLALCHEMY_DATABASE_URI'] = os.environ.get('DATABASE_URL', 'sqlite:///app.db')
app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False

db = SQLAlchemy(app)
login_manager = LoginManager()
login_manager.init_app(app)
login_manager.login_view = 'auth.login'

# Models
class User(UserMixin, db.Model):
    id = db.Column(db.Integer, primary_key=True)
    username = db.Column(db.String(80), unique=True, nullable=False)
    email = db.Column(db.String(120), unique=True, nullable=False)
    password_hash = db.Column(db.String(128))
    tasks = db.relationship('Task', backref='assigned_user', lazy=True)

class Task(db.Model):
    id = db.Column(db.Integer, primary_key=True)
    title = db.Column(db.String(200), nullable=False)
    description = db.Column(db.Text)
    status = db.Column(db.String(50), default='pending')
    assigned_user_id = db.Column(db.Integer, db.ForeignKey('user.id'))
    due_date = db.Column(db.DateTime)

# Routes
@app.route('/api/tasks', methods=['GET'])
@login_required
def get_tasks():
    tasks = Task.query.all()
    return jsonify([{
        'id': task.id,
        'title': task.title,
        'status': task.status,
        'assigned_user': task.assigned_user.username if task.assigned_user else None
    } for task in tasks])

if __name__ == '__main__':
    app.run(debug=True)

Debugging with AI

Error Analysis and Fixes

ChatGPT can help you debug code by analyzing error messages and suggesting fixes.

Debugging Prompt Example

I'm getting this error in my Flask app:

Traceback (most recent call last):
  File "app.py", line 45, in 
    db.create_all()
  File "/venv/lib/python3.9/site-packages/flask_sqlalchemy/__init__.py", line 963, in create_all
    self._execute_for_all_tables(app, bind, 'create_all')
sqlalchemy.exc.OperationalError: (sqlite3.OperationalError) table user already exists

My code:
[Include relevant code here]

How do I fix this and prevent it in the future?

Interactive Demo: ChatGPT Debugging

Click "Ask ChatGPT" to see how AI would debug this error...

Performance Optimization

Common Performance Issues

N+1 query problems
Missing database indexes
Inefficient algorithms
Memory leaks
Unnecessary database calls

Hands-On Exercise: AI-Assisted Project Planning

Designing a Blog System with ChatGPT

Intermediate

Objective

Use ChatGPT to design and plan a complete blog system, then implement the core functionality.

Steps

Create a comprehensive prompt for the blog system
Get ChatGPT to design the architecture
Generate database models and API endpoints
Implement the core functionality
Test and refine the implementation

Sample Prompt

Design a blog system with the following requirements:

Features:
- User registration and authentication
- Create, edit, delete blog posts
- Comment system on posts
- Tag system for categorizing posts
- Search functionality
- Admin panel for managing users and content

Technical requirements:
- Flask backend with SQLAlchemy
- RESTful API design
- JWT authentication
- PostgreSQL database
- File upload for images

Generate:
1. System architecture diagram
2. Database schema with relationships
3. API endpoint specifications
4. Flask application structure
5. Security considerations

Key Takeaways

💬

Effective Prompting

Clear, specific prompts with context and requirements produce the best results from AI tools.

🏗️

Architecture Design

Use AI to explore different architectural patterns and make informed design decisions.

🗄️

Database Modeling

AI can help design optimal database schemas and suggest indexing strategies.

🔧

Code Generation

Generate boilerplate code and starter templates to accelerate development.

Next Steps

Preparation for Chapter 3

Before moving to the next chapter, practice:

Create prompts for different types of projects
Use ChatGPT to design a simple application
Generate and test code snippets
Practice debugging with AI assistance
Explore different architectural patterns

Cross-References

Related Chapters

External Resources

Conclusion

ChatGPT is a powerful tool that can significantly enhance your development workflow when used effectively. By mastering the art of prompting and understanding how to integrate AI assistance into your development process, you can accelerate your learning and build better applications.

Remember: ChatGPT is a tool to enhance your skills, not replace them. The best developers use AI to amplify their capabilities while maintaining their critical thinking and problem-solving abilities.