AI for Developers

Most of a developer’s day is not the hard, interesting problem. It is the boilerplate, the third time writing the same kind of test, the half hour lost to one error message, and the documentation nobody wants to write. AI is at its best on exactly that layer.

It will not architect your system or understand your codebase the way you do, and its code looks right more often than it is right. Treat it as a fast first-drafter and a debugging partner, review everything it produces, and it earns its keep.

Chat Assistants, AI Editors, and Coding Agents

Developers now meet AI in three forms, and it helps to know which one you are reaching for.

Chat assistants like ChatGPT and Claude are where you paste code, describe a bug, or talk through a design. They are best for explanation and one-off problems.

AI editors like Cursor put suggestions and an agent mode inside the place you already type, so help arrives line by line.

Coding agents take a whole task, such as “add pagination to this endpoint and update the tests,” then read the codebase, edit files, run commands, and report back. Claude Code, Codex, OpenCode, and Meta Muse Code are the main examples. They work in the terminal, your IDE, or the cloud, and they are where most of the change in day-to-day development has happened. For a side-by-side look, see Codex vs Claude Code.

Agents bring two new habits worth building early. Give them standing project instructions, such as a CLAUDE.md or AGENTS.md file, so you are not repeating conventions in every task; see Set up CLAUDE.md, SKILL.md, and PITFALLS.md. And watch cost: long agent runs on the strongest models burn through usage limits, so send routine work to cheaper models with model routing. If code can’t leave your machine, a local model can handle a lot of the routine work.

Where AI Adds the Most Value for Developers

Writing Code Faster

AI handles boilerplate, common patterns, and routine implementations well. Use it to get to a working starting point, then refine.

Prompt to try:

Write a [language] function that [describe what it should do]. 
Requirements: [list any constraints]. 
Include basic error handling and a usage example.

Debugging

Describe what your code does, what it should do, and the error you’re seeing.

Prompt to try:

I'm getting this error: [paste error message].
Here's the relevant code: [paste code].
What's likely causing this and how would you fix it?

Code Review

Prompt to try:

Review this code for: correctness, potential bugs, edge cases, 
performance issues, and readability. 
Suggest specific improvements with explanations.
[Paste code]

Writing Documentation

Prompt to try:

Write clear, concise documentation for this function/module/API. 
Include: description, parameters, return values, error handling, 
and usage examples.
[Paste code]

Learning a New Technology

Prompt to try:

I know [existing language/framework]. Explain [new technology] 
using comparisons to what I already know. 
Focus on the mental model shift, not a full tutorial.

Writing Tests

Prompt to try:

Write unit tests for this function in [testing framework]. 
Cover: happy path, edge cases, error handling, and any obvious 
boundary conditions.
[Paste code]

Regex and Complex Queries

Prompt to try:

Write a regex pattern that matches [describe the pattern]. 
Test cases it should pass: [list examples]. 
Explain how it works.

Mistakes to Avoid

Don’t deploy AI-generated code without understanding it. If you don’t understand what the code does, you can’t debug it, maintain it, or explain it in a code review.

Don’t use AI for security-critical code without review. Authentication, encryption, input sanitization, and security logic require careful human review. AI code in these areas can introduce subtle vulnerabilities.

Don’t paste proprietary code into public AI services. Be aware of your organization’s data policies before pasting internal code into ChatGPT or Claude, or pointing a coding agent at a private repository. Use enterprise versions with appropriate data handling agreements when working with sensitive code.

Don’t give an agent more access than the task needs. A coding agent can run commands, so keep risky actions behind approval, run untrusted work in a sandbox, and review the diff before anything is merged.

Don’t trust AI on library APIs. AI sometimes confidently describes functions or parameters that don’t exist in the version you’re using. Always verify against official documentation.