Using AI for Coding and Debugging

AI has transformed how developers write and debug code. Tools like GitHub Copilot, ChatGPT, and Claude can generate functions, explain errors, suggest fixes, and even architect entire applications. For beginners, AI can be an always-available tutor. For experienced developers, it is a powerful accelerator.

But AI-generated code comes with risks that are different from the risks in AI-generated text. A factual error in an essay is embarrassing. A bug in production code can be catastrophic. This lesson covers how to use AI for coding effectively and safely.

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How AI Writes Code

AI models generate code using the same next-token prediction mechanism as they use for text. During training, they were exposed to billions of lines of code from open-source repositories, documentation, Stack Overflow, tutorials, and technical books.

This means AI is particularly good at:

• Common patterns: Code that appears frequently in training data (sorting algorithms, API calls, database queries) is generated reliably
• Well-documented languages and frameworks: Python, JavaScript, TypeScript, React, Django, Rails — anything popular with extensive documentation
• Boilerplate code: Repetitive setup code, configuration files, standard CRUD operations