The way we write code is changing. Large Language Models (LLMs) like Claude, ChatGPT, and Copilot are not just hype — they're fundamentally altering the development workflow. I've been using LLMs extensively for the past year, and the impact is real. Code is written faster, repetitive tasks disappear, and I spend more time on architecture and less on boilerplate.

But there's a catch: using LLMs well requires discipline. Blindly accepting generated code is dangerous. This post covers how I've integrated AI into my development practice in a way that improves quality rather than degrading it.

What LLMs Are Good At

Boilerplate and repetitive code: Setting up a new service, creating CRUD operations, scaffolding tests. LLMs excel at this because the patterns are well-established and there's tons of training data.

Documentation and comments: LLMs write clear, concise documentation. Ask an LLM to document your function, and it produces something better than most handwritten documentation.

Refactoring suggestions: "How would you improve this code?" LLMs suggest improvements quickly. They're not always right, but they're often thought-provoking.

Explaining code: Don't understand a complex algorithm? Ask an LLM. It explains clearly, with examples.

Writing tests: LLMs understand testing patterns. "Write comprehensive tests for this function." It produces good test coverage.

What LLMs Are Bad At

Complex domain logic: LLMs don't understand your business. "Implement our pricing logic" results in code that might work for simple cases but misses edge cases LLMs aren't aware of.

Architectural decisions: "Should we use microservices?" LLMs give balanced answers, not decisions. You decide.

Security-critical code: Authentication, encryption, financial calculations. Have a human review. LLMs can hallucinate or make subtle mistakes with serious consequences.

Code that works in one context but not yours: LLMs trained on generic code. Your codebase has unique patterns and constraints. Generated code sometimes doesn't match your patterns.

Practical Workflows for AI-Assisted Development

Workflow 1: Scaffold, Then Review

1. Ask LLM to create boilerplate (new class, test file, configuration)

2. Review what it generated. Fix anything that doesn't match your patterns

3. Add domain-specific logic manually

This is 3x faster than writing boilerplate manually.

Workflow 2: TDD with AI Help

1. Write test (you)

2. Ask LLM to write code that makes the test pass

3. Review code. Refactor if needed

4. Run test. Adjust if LLM didn't understand the requirement

The test ensures the LLM code is correct. This is safer than AI code without tests.

Workflow 3: Pair With AI for Documentation

1. Write the code

2. Ask LLM to write documentation

3. Review and adjust. LLM might have misunderstood something

Result: Better documentation, faster than writing it yourself.

Maintaining Code Quality

Always use version control: You should already be doing this, but with AI code, it's critical. Review diffs carefully.

Run linters and tests: AI-generated code doesn't always follow your patterns. Linters catch style issues. Tests catch logic problems.

Code review for AI code is non-negotiable: Don't merge AI code without review. Review more carefully than handwritten code, actually — LLMs make different mistakes than humans.

Keep domain logic human-written: The code that matters most — business logic, security, critical paths — should be written by humans who understand your domain. AI handles the mechanical parts.

Document AI usage: If a function was AI-generated, add a comment. Future maintainers should know.

The Mindset Shift

Using LLMs well means thinking differently about development. You're not writing every line yourself — you're orchestrating. You guide the AI with clear requirements, review its work, and integrate it into your codebase.

This is powerful, but only if you maintain high standards. Bad code written by you is still bad code. Bad code written by AI is worse because you might not notice the problems.

The Near Future

AI tools are improving rapidly. Soon, AI will handle more complex tasks. But I suspect the human element will remain crucial — architects and reviewers deciding what to build and validating that it's built correctly.

The developers who thrive will be those who integrate AI into their workflow thoughtfully, maintaining quality while gaining speed. The alternative — blindly trusting AI — leads to mediocre codebases full of subtle bugs.

AI is not a replacement for developers. It's a tool that multiplies the impact of good developers. Use it wisely.