Coding with AI Powered Tools
Expert answers about AI coding tools, quality assurance, and best practices for AI-powered development.
TOC
How to ensure code quality?
When you follow best practices, code quality and security improve. This includes having AI do separate peer reviews for both quality and security. You should also run your standard code security and quality tools alongside AI reviews. Human peer reviews is also recommended, but over time teams will not need to spend as much time on reviews as they learn to trust AI’s capabilities.
The best AI coding tool
This chart was generated using ChatGPT 5 on 9/12/2025.
| Capability | Claude Code |
GitHub Copilot |
Cursor |
Windsurf |
Grok CF1 |
Gemini CLI |
OpenAI Codex |
|---|---|---|---|---|---|---|---|
| Proof / Maturity | 8 | 9 | 8 | 7 | 6 | 6 | 7 |
| Inline completions | 8 | 9 | 9 | 9 | 8 | 8 | 7 |
| Writing tests | 9 | 8 | 8 | 8 | 8 | 7 | 7 |
| Writing specs | 9 | 7 | 8 | 8 | 7 | 8 | 6 |
| Debugging code | 8 | 8 | 8 | 8 | 7 | 8 | 6 |
| Architectural specs | 9 | 7 | 8 | 8 | 6 | 7 | 5 |
| Live system debug | 8 | 6 | 7 | 8 | 6 | 7 | 5 |
| Pair coding | 9 | 8 | 9 | 9 | 8 | 8 | 6 |
| System review | 9 | 7 | 8 | 8 | 6 | 8 | 5 |
| Autonomous coding | 9 | 7 | 8 | 9 | 8 | 8 | 5 |
| Documentation | 9 | 8 | 8 | 8 | 7 | 8 | 6 |
| Security/Compliance | 9 | 7 | 7 | 7 | 6 | 7 | 5 |
| Legacy code | 8 | 7 | 8 | 8 | 7 | 7 | 5 |
| Team collaboration | 9 | 8 | 8 | 8 | 7 | 8 | 6 |
| Capability | Claude Code |
GitHub Copilot |
Cursor |
Windsurf |
Grok CF1 |
Gemini CLI |
OpenAI Codex |
|---|---|---|---|---|---|---|---|
| Proof / Maturity | 8 | 9 | 8 | 7 | 6 | 6 | 7 |
| Inline completions | 8 | 9 | 9 | 9 | 8 | 8 | 7 |
| Writing tests | 9 | 8 | 8 | 8 | 8 | 7 | 7 |
| Writing specs | 9 | 7 | 8 | 8 | 7 | 8 | 6 |
| Debugging code | 8 | 8 | 8 | 8 | 7 | 8 | 6 |
| Architectural specs | 9 | 7 | 8 | 8 | 6 | 7 | 5 |
| Live system debug | 8 | 6 | 7 | 8 | 6 | 7 | 5 |
| Pair coding | 9 | 8 | 9 | 9 | 8 | 8 | 6 |
| System review | 9 | 7 | 8 | 8 | 6 | 8 | 5 |
| Autonomous coding | 9 | 7 | 8 | 9 | 8 | 8 | 5 |
| Documentation | 9 | 8 | 8 | 8 | 7 | 8 | 6 |
| Security/Compliance | 9 | 7 | 7 | 7 | 6 | 7 | 5 |
| Legacy code | 8 | 7 | 8 | 8 | 7 | 7 | 5 |
| Team collaboration | 9 | 8 | 8 | 8 | 7 | 8 | 6 |
When it comes to writing production code, you need the best tool the world has to offer, period. Trying to save money on a lesser or unproven tool is foolhardy. I have found that I not only need top quality, but also flexibility.
I’m oversimplifying, but GitHub Copilot is fantastic for pair programming and helping to write functions. Cursor takes that farther in the direction of being able to write programs. But since May of 2024, Claude Code has been the leader with regards to code quality, as well as flexibility. It is top-notch at writing specs, programs, test suites, documentation, and doing system admin work.
Competitors are catching up like OpenAI’s O3, Gemini CLI, and Grok CLI. But they are playing catch-up and they are not yet proven. In the meantime, Anthropic will continue to improve Claude Code.
Assuming that the current leader will always stay the leader would be foolhardy, but the safe and smart bet currently is Claude Code. It’s highly recommended that you use a combination of the Claude Code plugin for VS Code as well as the command line interface.
I personally have thousands of hours of experience making production-quality code using Claude Code, and I have not once found a puzzle I couldn’t crack.
When do AI tools work best?
If a clear spec can be written, then the project can be done efficiently with AI. The spec can be written by AI and tweaked and reviewed by the human. Adding a typical feature to an existing software system or fixing a bug tends to be very efficient with AI programming tools.
The places where there’s not as high a performance boost is where you can’t write a clear spec for what you want. Where it’s more subjective or many different stages of iteration will be needed. If you don’t know quite what you want, AI is likely to be less efficient at delivering it.
We’ve been trained to think that “expensive” means “it’s hard and will take a long time”. But with AI programming, the things that are traditionally the most expensive tend to be some of the easiest tasks for AI tools to code.
Who owns AI-generated code?
In the Terms of Service for all of the main tools you are likely to be using, the customer owns the results. There’s no ambiguity there. You own the code that AI generates.
Is my data and code safe?
With regards to security (knowing that your data is safe), the gold standard in the US is SOC compliance. For enterprise purposes, all of the main tools you might be using list themselves as SOC 2 Type II compliant. And I trust that they’re telling the truth, with the potential exception of Grok.
Note that I put a robust system in place to run SOC 2 Type II audits and have run several of them myself using the system that I created.
In short, SOC 2 Type II means that there are many security protocols in place, including yearly audits, to ensure that your data is as safe as possible.
How to maintain quality?
When using best practices, code quality will increase, not decrease. You’ll use AI to review code for both quality and security, alongside human oversight and code reviews.
Over time, you’ll likely require fewer human code reviews as you learn to trust the AI-generated code. However, every single line of code that gets generated should be tested properly with a comprehensive test suite. The combination of AI reviews, automated testing, and selective human oversight creates a quality framework that’s actually stronger than traditional development approaches.
The key is that AI doesn’t replace quality controls; it enhances them. You get more consistent reviews, better test coverage, and AI will consistently abide by whatever coding standards you tell it to.