💻 AI-Assisted Coding
Learn AI-Assisted Coding on AI4AI — short, hands-on lessons with live AI runs, at three reading levels (beginner to expert). Free to start.
What you'll learn
- The 2026 AI Coding Landscape: Assistants, Completions, and Agents (8 min) — ⚡ In 2026, AI coding tools fall into three distinct categories, each suited to different tasks. **Chat assistants** (e.…
- Prompting for Code That Works (7 min) — ⚡ A vague prompt like 'write a function to parse dates' forces the model to guess your language, framework, input forma…
- Agentic Coding: AI That Plans, Edits, Tests, and Self-Corrects (8 min) — An agentic coding workflow replaces the single-turn prompt-response pattern with an autonomous loop. The AI agent recei…
- Reviewing AI-Written Code: Bugs, Security Holes, and Hallucinated APIs (8 min) — AI code generators (GitHub Copilot, Claude, GPT-4o, Gemini) produce plausible-looking code quickly, but they introduce …
- Debugging with AI: From Stack Trace to Root Cause (7 min) — When your program crashes, the runtime produces a stack trace — a list of function calls, file names, and line numbers …
- Staying in Control: Tests, Version Control, and Guardrails for AI-Assisted Coding (8 min) — ⚡ AI coding assistants (Copilot, Cursor, Claude, etc.) can generate large amounts of code quickly, but speed creates ri…
Lessons
The 2026 AI Coding Landscape: Assistants, Completions, and Agents
⚡ In 2026, AI coding tools fall into three distinct categories, each suited to different tasks. **Chat assistants** (e.g., ChatGPT, Claude, Gemini via web or API) are conversational interfaces where you describe a problem, paste code, or ask for explanations. They excel at open-…
Prompting for Code That Works
⚡ A vague prompt like 'write a function to parse dates' forces the model to guess your language, framework, input format, and error-handling style — and it will guess wrong at least some of the time. A well-structured prompt removes that guesswork. Three ingredients make a codin…
Agentic Coding: AI That Plans, Edits, Tests, and Self-Corrects
An agentic coding workflow replaces the single-turn prompt-response pattern with an autonomous loop. The AI agent receives a high-level goal (e.g., 'add pagination to the user list endpoint and make all tests pass'), then independently performs a sequence of actions: reading rel…
Reviewing AI-Written Code: Bugs, Security Holes, and Hallucinated APIs
AI code generators (GitHub Copilot, Claude, GPT-4o, Gemini) produce plausible-looking code quickly, but they introduce three distinct failure classes you must catch before shipping. **Hallucinated APIs** are the most insidious: the model invents a method or library that does not…
Debugging with AI: From Stack Trace to Root Cause
When your program crashes, the runtime produces a stack trace — a list of function calls, file names, and line numbers leading to the error, plus an exception type (e.g., `TypeError`, `NullPointerException`, `segfault`). Pasting this directly into an AI model, along with the rel…
Staying in Control: Tests, Version Control, and Guardrails for AI-Assisted Coding
⚡ AI coding assistants (Copilot, Cursor, Claude, etc.) can generate large amounts of code quickly, but speed creates risk: the model may introduce subtle bugs, remove edge-case handling, or produce code that looks correct but breaks under real conditions. Three practices keep yo…