2.5 – How to Troubleshoot AI Code & Get Past Issues Lesson

Lesson Overview

AI-assisted coding can speed up development and open new possibilities, but it isn’t foolproof. Sooner or later, you’ll run into situations where something doesn’t work as expected—maybe a feature that used to work is now broken, or a new functionality never functioned at all. This lesson’s focus is on equipping you with reliable, repeatable troubleshooting techniques so that you can diagnose and overcome issues with AI-generated code.

You’ll learn how to efficiently isolate what’s changed in your code, communicate issues clearly to the AI, and use logs and error messages to pinpoint the root cause. The lesson introduces the “beaver method,” where you guide the AI in adding logging throughout your code, allowing for a more precise, step-by-step investigation. We also cover how to handle situations when nothing seems to work by prompting the AI for completely new solutions.

These strategies are especially valuable for entrepreneurs who may not have formal development experience, as well as anyone relying on AI to build products or automate tasks. Whether you’re seeing UI problems, hidden errors, or fuzzy logic, being able to methodically troubleshoot ensures your projects keep moving forward.

Who This Is For

Troubleshooting AI-assisted code is relevant to a wide range of learners and professionals. This lesson will be especially useful for:

Startup founders and solo entrepreneurs using AI to speed up product development

• Non-technical creators building apps or tools with AI app builders
• Product managers or team leads who guide AI-driven projects
• Hobbyists experimenting with AI-generated code in personal projects
• Small business owners working to automate internal processes
• Anyone who wants to feel more confident fixing issues when AI code fails

Where This Fits in a Workflow

Troubleshooting comes into play any time AI-generated code produces errors or behaves unexpectedly. You’ll use these skills after generating new features, installing updates, or integrating changes suggested by an AI assistant. For example, after letting AI add user registration to your app, you notice the signup page looks broken. This is where you’d apply these troubleshooting steps: checking what changed, reviewing error logs, and possibly using the “beaver method” to add more insight.

Another common scenario is creating a new feature from scratch that never quite works—the methods covered here help you communicate clearly with the AI, provide meaningful context, and encourage more successful iterations. These troubleshooting habits will save you time and frustration in nearly every AI-driven coding workflow.

Technical & Workflow Benefits

Traditional debugging can be challenging, especially for those without coding backgrounds. Manually inspecting code for problems is slow and can be intimidating. In contrast, the troubleshooting approach in this lesson leverages AI’s strengths, letting you:

• Quickly isolate and communicate what changed in your code, speeding up the resolution process
• Use targeted prompting and clear context (such as logs and screenshots) to get better AI assistance
• Automate the process of adding logs with the “beaver method,” providing a detailed look without manual edits
• Experiment with alternative code solutions, increasing your chances of unblocking tough issues
• Build your understanding of both high-level software behavior and line-by-line operations, leading to fewer issues in the future

For entrepreneurs, this means solving more problems independently, reducing reliance on external developers, and shipping updates faster.

Practice Exercise

Choose a small feature in an AI-generated project that is not working as expected (or purposefully break a feature). Follow these steps:

1. Identify what has changed before and after the issue appeared. Make note of any features that still work and ones that don’t.
2. Use error messages, screenshots, or logs—copy and paste these into your AI tool, asking for an explanation or fix. Try the “beaver method” by asking AI to insert logs at key points in your code, then rerun the feature and gather the output for further troubleshooting.

Reflection: Did providing specific information and using the beaver method help the AI give you a more accurate or helpful solution? How did the process compare to simply asking the AI to “fix my code”?

Course Context Recap

This troubleshooting lesson builds directly on the earlier strategies for working with AI-generated code. Previously, you learned how to prompt AI tools for new features and understood the basics of integrating their suggestions. Troubleshooting skills come into play when results aren’t what you expect, giving you confidence to move past roadblocks. Up next, you’ll explore the key components that make up a full stack product—putting you one step closer to building and polishing your own AI-powered project. Continue learning and apply what you’ve practiced in the next part of the course.