How to Troubleshoot Common Issues with AI Coding Assistants in Under 30 Minutes

If you've been using AI coding assistants in 2026, you've likely run into a few hiccups. These tools can be incredibly powerful, but they don't always provide the perfect solution on the first try. As indie hackers and solo founders, time is money, and spending hours debugging AI-generated code isn't an option. In this guide, I’ll walk you through some common issues with AI coding assistants and how to troubleshoot them quickly.

Time Estimate and Prerequisites

• Time: You can finish this in about 30 minutes.
• Prerequisites:
  • A working AI coding assistant (e.g., GitHub Copilot, Tabnine, etc.)
  • Basic understanding of the programming language you're using.
  • Access to your project codebase.

Common Issues and Quick Fixes

1. AI Suggests Incorrect Code

Expected Output: Code that compiles without errors.

Troubleshooting Steps:

• Rephrase Your Query: Often, the AI might not understand your request. Try rephrasing it for clarity. For example, instead of "create a function," say "write a function that calculates the sum of two numbers."
• Check Context: Ensure that the AI has enough context about your code. If you're working within a specific class or function, make sure to include that context in your prompt.

2. Missing Dependencies or Libraries

Expected Output: Code that runs with all necessary imports.

Troubleshooting Steps:

• Add Imports Manually: If the code suggestions are missing imports, you can quickly add them based on the error messages you receive.
• Library Documentation: Check the documentation for any libraries you're using to ensure compatibility with the AI’s suggestions.

3. Syntax Errors

Expected Output: Clean, error-free code.

Troubleshooting Steps:

• Use Linting Tools: Integrate a linter like ESLint (for JavaScript) or Pylint (for Python) to catch syntax errors. Most IDEs can highlight these in real-time.
• Test Incrementally: Instead of running the entire block of AI-generated code, test small snippets to isolate errors.

4. Inefficient Code

Expected Output: Optimized code that runs efficiently.

Troubleshooting Steps:

• Profile Your Code: Use profiling tools specific to your programming language to identify bottlenecks.
• Ask for Refactoring: If you notice inefficiencies, you can prompt the AI to improve the code. For instance, ask, "Can you optimize this function for speed?"

5. Incomplete Functionality

Expected Output: Fully functional features as per requirements.

Troubleshooting Steps:

• Break Down the Problem: If the AI’s output is incomplete, break your request into smaller tasks. For instance, instead of asking for a complete user authentication system, ask for the login functionality first.
• Iterate and Improve: Use the AI’s output as a starting point. Test it, then ask for additional features or modifications based on what you need.

Troubleshooting Checklist

| Issue | Steps to Troubleshoot | Expected Outcome | |-----------------------------|------------------------------------------------|--------------------------------------| | Incorrect Code Suggestions | Rephrase query, check context | Correct code | | Missing Dependencies | Add imports manually, check documentation | Code runs without errors | | Syntax Errors | Use linting tools, test incrementally | Clean, error-free code | | Inefficient Code | Profile your code, ask for refactoring | Optimized code | | Incomplete Functionality | Break down the problem, iterate and improve | Fully functional features |

What We Actually Use

In our experience, we primarily rely on GitHub Copilot and Tabnine for coding assistance. Both tools have their strengths:

• GitHub Copilot: Great for general coding suggestions but can struggle with context-heavy tasks. Pricing starts at $10/month after a free trial.
• Tabnine: Excellent for autocomplete and quick fixes, especially if you’re working in a team. Pricing starts at $12/month for the Pro version.

Limitations

• GitHub Copilot can sometimes produce verbose code that requires further simplification.
• Tabnine may not support as many languages as other tools.

Conclusion: Start Here

When troubleshooting AI coding assistants, remember to keep your requests clear and to leverage tools that enhance your coding experience. Start with the quick fixes mentioned here, and don’t hesitate to iterate on the AI's outputs. You can save time and make your development process smoother by handling common issues efficiently.

For a deeper dive into practical tools and strategies, check out our weekly podcast, Built This Week, where we share insights on tools we're testing and products we're shipping.