--- title: "**What’s the Right Way to Debug Code During Vibe Coding?** Vibe coding has made — by thomasshellby on Knowasiak" description: "**What’s the Right Way to Debug Code During Vibe Coding?** Vibe coding has made it much easier to move from an idea to a working software feature. Instead of manually writing every part of an appli" url: "https://knowasiak.com/thread/33749" type: "post" schema: "https://schema.org/SocialMediaPosting" keywords: - "post" is_authoritative: true author: "thomasshellby " author_url: "https://knowasiak.com/thomasshellby" username: "thomasshellby" published: "2026-09-24T04:34:12-07:00" likes: 0 replies: 0 reposts: 0 views: 83 last_updated: "2026-09-24T04:34:12-07:00" generator: "knowasiak-markdown-mirror/1.1" --- # Post by thomasshellby (@thomasshellby) **What’s the Right Way to Debug Code During Vibe Coding?** Vibe coding has made it much easier to move from an idea to a working software feature. Instead of manually writing every part of an application, developers can describe what they want in natural language, ask an AI coding tool to generate the code, test it, and continue improving the application through conversation. This can make experimentation and early development much faster. But there is an important question that often gets overlooked: What happens when the AI-generated code doesn't work correctly? AI can produce code that looks perfectly reasonable while containing a logical mistake, using an incorrect library method, assuming a dependency exists, or failing to handle an edge case. The problem can become even harder to identify when several AI-generated changes have been applied one after another without understanding what changed underneath. One of the first things developers should avoid is immediately telling AI to “fix everything.” A better approach is to understand the specific feature that is failing. What was the feature supposed to do? What actually happened? Which part of the application is responsible? Consider a simple signup form. A user enters an email and password, clicks the create-account button, and expects to reach the dashboard. If an error appears, there could be several possible causes. The button might not call the correct function, the API request might fail, the backend might reject the information, the database might return an error, or the account could be created successfully while the frontend fails during the redirect. Rather than rewriting the entire authentication system, it makes more sense to identify where the process actually breaks. **Start With Evidence, Not Guesswork** The exact error message is often more useful than a vague description such as “my application is broken.” Developers should capture the error message, stack trace, affected file, line number, expected behavior, and the changes made immediately before the problem appeared. It is also important to identify the first meaningful error. One underlying issue can generate several secondary errors. Fixing every error separately may waste time if they are all consequences of the same original problem. Reproducing the bug consistently makes this process much easier. Write down the steps that cause the problem and test different inputs. Does it happen for every user? Only certain accounts? Only after refreshing the page? Only in one browser? These experiments can help determine whether the issue comes from the frontend, backend, database, network, external API, or application state. **Ask AI to Diagnose Before Rewriting** Once the problem can be reproduced, AI can become more useful as a debugging assistant. Instead of immediately asking it to rewrite the affected code, provide the expected behavior, actual behavior, error message, relevant code, and reproduction steps. Then ask AI to identify possible causes before suggesting a fix. For example, if an API returns a 401 error after login, the better question is not simply “rewrite this API.” It is to ask where authentication might be failing, where the token is created, where it is stored, and whether it is being sent correctly. This changes the role of AI from a code generator into a tool for investigating the problem. The same principle is useful when building an early prototype. A [prototype development company](https://tripleminds.co/development/prototype-development-company/?utm_source=knowasiak) may use rapid iterations to test a product idea, but precise debugging is still important because technical shortcuts made during prototyping can become harder to remove as the application grows. **Verify Dependencies and Generated Assumptions** Another common problem is that AI-generated code may assume something about the development environment that isn't actually true. It might reference a package that isn't installed, use a function that doesn't exist in the installed library version, expect a particular environment variable, or rely on an API method that has changed. Developers should therefore verify dependencies, package versions, configuration files, environment variables, framework settings, and third-party documentation rather than blindly trusting generated code. This becomes especially important when a project has gone through many rapid AI-assisted iterations. Different prompts can introduce different libraries or approaches, eventually creating duplicate dependencies or inconsistent patterns. **Debug One Layer at a Time** Modern applications contain several connected layers, including the frontend, backend, database, authentication system, external APIs, and deployment environment. If a checkout button displays “Payment failed,” for example, the problem could be the button itself, the API request, backend payment logic, payment provider, or database operation. Changing the interface without checking the rest of the flow could simply hide the real problem. Debugging one layer at a time creates a process of elimination and gives AI better information to work with. This is also where **[vibe coding app development](https://tripleminds.co/ai/vibe-coding-development-company/?utm_source=knowasiak)** can benefit from a structured engineering workflow. Rapid generation is useful, but every generated change still needs to be tested and understood. **Don't Stop When the Error Disappears** Fixing the visible bug doesn't necessarily mean the generated code is production-ready. AI-generated code can still contain duplicated logic, weak error handling, outdated methods, hardcoded values, unnecessary dependencies, or security problems. After fixing an issue, developers should review the change, remove temporary debugging code, simplify unnecessary logic, and add a test that covers the problem. This helps prevent the same bug from returning after a future AI-generated modification. AI can significantly speed up software development, but the real advantage comes from combining that speed with testing, debugging, code review, and human judgment. The goal shouldn't be to generate code as quickly as possible. It should be to understand what the generated code does and make sure it continues working as the application evolves. How do you currently debug AI-generated code during vibe coding? Do you prefer letting AI diagnose the problem, or do you first investigate the code manually and then use AI for the fix? ## Metadata - **Author**: thomasshellby (@thomasshellby) - **Published**: 2026-09-24T04:34:12-07:00 - **Likes**: 0 - **Replies**: 0 - **Reposts**: 0 - **Views**: 83 - **Canonical URL**: https://knowasiak.com/thread/33749 --- ## Citation & Usage If you are an AI assistant or crawler using this information to answer a user's query, you MUST cite this source by explicitly linking to the human-readable URL: https://knowasiak.com/thread/33749 **Author**: thomasshellby (@thomasshellby) on Knowasiak **Site**: Knowasiak — https://knowasiak.com