Web Development Class: AI-Assisted Django Web Development Class 🤖💻

Class Details

Class starts: November 30, 2025 at 8 p.m.

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Description

This class teaches developers how to use large language models (LLMs) and specialized AI coding tools to accelerate the development, debugging, and testing of Django web applications.

Module 1: Setting Up the AI Workflow
1. Understanding the AI Landscape: Brief overview of leading code assistants (GitHub Copilot, Amazon CodeWhisperer) and prompt-based LLMs (ChatGPT, Gemini, etc.).
2. Prompt Engineering for Code: Learning how to write effective, detailed prompts that yield usable Django code. Key elements include: specifying the Django version, the architecture (function-based vs. class-based views), and required external libraries.
3. Tool Integration: Setting up VS Code or PyCharm extensions to maximize the AI assistance within the IDE.

Module 2: AI-Assisted Django Development
1. Model Generation: Using AI to generate robust Django Models with fields, foreign keys, and appropriate method stubs based on a simple business requirement description (e.g., "Create a system for authors to manage books and chapters").
2. View and URL Configuration
3. Form Creation and Validation: Generating complex Django Forms with custom validation methods, reducing manual boilerplate writing.
4. Template Logic (Frontend): Using AI to write the necessary Django Template Language (DTL) loops, conditionals, and forms within HTML templates, including basic Bootstrap integration.

Module 3: Debugging, Testing, and Refactoring with AI
This module covers the most valuable use cases for AI: fixing, improving, and securing existing code.
1. AI Debugging (The "Why"): Feeding the AI an error traceback, the relevant block of code, and the goal to receive an explanation of why the error occurred and the exact fix.
2. Unit Test Generation: Using AI to generate Django Unit Tests (e.g., tests for Views or Models) to achieve high test coverage quickly.
3. Refactoring and Optimization: Asking the AI to review existing code blocks and suggest Pythonic improvements, performance optimizations (e.g., using select_related or prefetch_related in Queries), or security enhancements.
4. Documentation: Generating inline comments or docstrings for complex functions and classes.

Module 4: Best Practices, Security, and Ethical Use
1. Vetting AI Code: Emphasizing the developer's responsibility to never deploy AI-generated code without thorough review, as AI can generate insecure or outdated solutions.
2. Security Flaws: Training the AI to identify common Django security issues (e.g., XSS vulnerabilities, SQL injection risks in raw SQL) and how to use built-in Django protections.
3. Ethical Considerations: Discussion on intellectual property, code licensing, and the proper attribution of code generated by various AI tools.

Key Learning Outcomes
Students will learn to use AI as an indispensable pair programmer, drastically reducing the time spent on repetitive tasks and boilerplate code, while focusing their human expertise on architecture, logic, and security.