How to Debug Code with AI Tools in 60 Minutes

Debugging can be a real headache for developers, especially when you're racing against the clock to meet a deadline. In 2026, AI tools have become increasingly capable of helping us through this process, but knowing which tools to use and how to leverage them effectively can be daunting. In this guide, I'll show you how to debug your code using AI tools in just 60 minutes, sharing specific tools, pricing, and my personal experiences along the way.

Prerequisites: What You Need Before You Start

Before diving into the debugging process, make sure you have the following:

1. A codebase ready for debugging: This could be a personal project or a side hustle.
2. Basic understanding of the programming language you're using: Familiarity helps you make sense of AI recommendations.
3. An AI debugging tool: Choose from the tools listed below.

Step-by-Step Debugging Process

Step 1: Choose Your AI Debugging Tool

There are several AI debugging tools available, each with its unique strengths. Here’s a breakdown of some popular options:

| Tool Name | Pricing | Best For | Limitations | Our Take | |-------------------|-----------------------|----------------------------|------------------------------------|----------------------------| | DeepCode | Free tier + $19/mo pro | Java, JavaScript, Python | Limited for languages like Ruby | We use this for JavaScript | | Tabnine | Free + $12/mo pro | Code completion and suggestions | Contextual understanding can falter | We use this for Python | | Sourcery | Free + $10/mo pro | Python refactoring | Limited to Python only | We don’t use this because we prefer DeepCode | | CodeGuru | Starts at $19/mo | Java and Python | Not as intuitive for beginners | We tried it but found it lacking in suggestions | | Kite | Free + $19.90/mo | Python and JavaScript | Limited language support | We don’t use this because it’s mostly for Python | | AI Code Reviewer | $30/mo | Code review and feedback | Can be slow on larger codebases | We love this for code reviews |

Step 2: Set Up Your Environment

1. Install the tool: Follow the installation instructions specific to the AI debugging tool you chose.
2. Integrate with your IDE: Most tools offer plugins for popular IDEs like VS Code, IntelliJ, or PyCharm.
3. Load your codebase: Open your project in the IDE.

Step 3: Start Debugging

1. Run the AI tool: Use the tool's command to analyze your code. For example, in DeepCode, you would click on the "Analyze" button.
2. Review suggestions: The tool will provide you with a list of potential issues. Take note of both critical errors and warnings.
3. Fix the issues: Implement fixes based on the suggestions. Don’t blindly trust the AI—always review changes.

Expected Outputs

By the end of this step, you should have a cleaner codebase with fewer errors and warnings. You may also have a better understanding of how to prevent similar issues in the future.

Troubleshooting Common Issues

• Tool not recognizing issues: Ensure your code is properly formatted and that the tool supports the language and framework you're using.
• Slow performance: If the tool lags, try closing other applications or increasing your system’s performance capabilities.
• Inaccurate suggestions: AI tools aren't perfect. Use your judgment and experience to validate suggestions.

What's Next?

Once you've debugged your code, consider these next steps:

1. Integrate AI tools into your regular workflow: Make them a part of your coding routine to catch errors early.
2. Learn from the suggestions: Understand why certain errors occurred to improve your coding skills.
3. Explore further AI capabilities: Many tools offer features for code optimization and security checks.

Conclusion: Start Here

To effectively debug your code in 60 minutes using AI tools, begin by selecting the right tool for your specific needs. Based on my experience, DeepCode is a solid choice for JavaScript and Python. Follow the steps outlined above, and you’ll not only debug your code faster but also enhance your coding skills in the process.

What We Actually Use

In our experience, we primarily use DeepCode and Tabnine for our debugging needs. They provide the best balance between functionality and ease of use for our projects.