Development & Code AI Tools

AI Coding Tools: What's Actually Useful?

Every developer I know has a complicated relationship with AI coding assistants. Half the time these things feel like magic—autocompleting entire functions, explaining legacy code that nobody documented, catching bugs before they happen. The other half? They're confidently suggesting code that doesn't compile or has security holes you could drive a truck through.

That's the reality of AI dev tools in 2026. Genuinely useful, genuinely flawed, and you need to know which is which.

Where AI Actually Helps

Boilerplate. God, the boilerplate. If I never have to manually write another CRUD endpoint or basic auth flow again, that's reason enough to use these tools. AI handles repetitive patterns really well because... well, they're patterns. Predictable. The kind of thing you've written a hundred times before.

Code explanation is underrated. Got handed a legacy codebase with zero documentation? Point an AI at it and ask what's happening. It won't always be right, but it'll give you a starting point way faster than reading through spaghetti code line by line.

The autocomplete has gotten scary good. Not just finishing your current line—predicting what you're building and suggesting the next 10 lines. When it works, you feel like you're pair programming with someone who reads your mind. When it doesn't, you just keep typing. No big deal.

Where It Falls Apart

Anything novel or domain-specific. AI tools learned from existing code, so they're great at common patterns and terrible at anything unusual. Building something that doesn't look like typical GitHub repos? Expect less useful suggestions.

Security is a real concern. I've seen AI suggest SQL injection vulnerabilities, hardcoded credentials, and authentication bypasses. Looks fine at first glance. Would pass a casual code review. Would also get you hacked. Never trust AI-generated code without reviewing it like you'd review a junior dev's pull request.

Architecture decisions? Forget it. AI can implement what you describe, but it won't tell you if your approach is fundamentally flawed. The big-picture thinking is still on you.

Different Flavors of AI Dev Tools

Code Completion

Lives in your editor, suggests code as you type. Ranges from "slightly smarter autocomplete" to "basically writes the function for you." The good ones learn your codebase and style over time.

Code Review Bots

Automated review that catches more than traditional linters. Can find logic bugs, security issues, performance problems. Some even suggest fixes. Not a replacement for human review, but a useful first pass.

Documentation Generators

Takes your undocumented code and writes docs for it. Quality varies wildly. Best for generating first drafts that humans then edit. Don't just blindly accept what it produces.

Testing Assistants

Generates unit tests, identifies edge cases, helps maintain coverage. Actually pretty good for straightforward functions. Struggles with complex integration scenarios.

No-Code Builders

Describe what you want, get working code. Surprisingly capable for MVPs, internal tools, simple apps. You'll hit walls eventually, but for prototyping? Legitimately useful.

Making These Tools Work For You

Treat them as assistants, not autopilot. Review everything. Seriously, everything. The time you save writing code gets partially spent verifying it, but you're still net positive if you do it right.

Learn to prompt well. "Write a function" gets garbage. "Write a Python function that validates email format, handles edge cases like plus addressing, raises ValueError with descriptive message on invalid input"—now we're talking.

Know when to ignore suggestions. Sometimes AI confidently offers the wrong approach. Your experience tells you it's wrong. Trust that instinct. The tool is a helper, not an authority.

Common Questions