How to Use AI for Coding

AI has become the most significant productivity shift in software development since Stack Overflow. But there's a wide gap between developers who use it well — shipping code 2–3x faster without quality regressions — and developers who use it poorly, spending more time debugging AI mistakes than they would have spent writing the code themselves. The difference is almost entirely in how they prompt. Here's what works.

What AI Coding Tools Actually Excel At

AI coding assistants perform best on tasks with clear inputs and outputs: generating boilerplate for familiar patterns, explaining unfamiliar code, writing unit tests for an existing function, converting between languages, and drafting documentation. They're significantly less reliable on novel architecture decisions, security-sensitive code, complex concurrency, and anything that requires understanding your full codebase without it being provided as context. The developers who get the most value treat AI as a knowledgeable junior engineer: fast, capable on routine work, sometimes overconfident, and always requiring code review before anything goes to production.

How Context Makes or Breaks Code Generation

The single biggest quality driver for AI-generated code is the amount of context you provide. 'Write a function to parse this CSV' produces generic code with generic assumptions. 'Write a TypeScript function that parses a CSV of user records with columns: id (UUID), email, createdAt (ISO 8601 timestamp), and optionally a role field that defaults to user. Handle malformed rows by logging a warning and skipping them. Use no external libraries.' produces something close to production-ready. Include: the language and version, the framework if relevant, your naming conventions, performance or security constraints, and what existing code it needs to integrate with. The more context you give, the less debugging you'll do.

Debugging With AI: What to Include

When asking AI to debug code, paste the exact error message, the full stack trace, the relevant code section, and a description of what the code is supposed to do vs. what it's actually doing. 'My function isn't working' gives the AI nothing to reason from. 'This Python function throws a KeyError on line 14 when the input dictionary has a nested key that's None — here's the traceback: [paste]' gives it everything it needs. Also tell the AI what you've already tried — this prevents it from suggesting the same approaches that haven't worked and helps it reason toward what you haven't checked yet.

Code Review and Security Considerations

Always review AI-generated code before committing it — not as a formality, but because AI coding mistakes follow distinct patterns you can learn to spot. Watch for: unused imports and variables, hardcoded values that should be environment variables, missing error handling in async operations, SQL queries vulnerable to injection when parameters are interpolated directly, missing input validation at function boundaries, and logic that seems reasonable but doesn't handle edge cases. Security-sensitive code (authentication, authorization, payment handling, PII processing) should get extra scrutiny — AI is particularly prone to generating plausible-looking but insecure patterns in these domains.

Prompt for Security Review

After generating security-sensitive code, follow up with: 'Review this code for security vulnerabilities. Focus on: input validation, SQL injection, authentication bypass, and any sensitive data that might be logged or exposed. List any concerns with severity rating.'

Test Generation and Documentation

Two of the highest-ROI uses of AI for developers are test generation and inline documentation — both are often skipped under time pressure and both benefit enormously from automation. For tests, paste your function and ask: 'Write comprehensive unit tests for this function. Cover: the happy path, edge cases (empty input, null values, type mismatches), and error conditions.' For documentation, paste your function and ask: 'Write a JSDoc comment for this function that explains what it does, each parameter with its type and purpose, the return value, and any exceptions it can throw.' Both save 15–30 minutes per function and make your codebase meaningfully better.

Staying in Control: When Not to Use AI

Knowing when not to use AI code generation is as important as knowing when to use it. Avoid it for: decisions that require understanding your full system architecture (AI can't see it); refactoring tasks where correctness depends on tracing all usages of a symbol across the codebase; cryptographic implementations (use audited libraries, never hand-roll); and any code where you'd have no idea how to verify the output is correct. The developers who get hurt by AI coding tools are the ones who paste code they don't understand into production. If you can't explain what the generated code does, you shouldn't ship it.