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The Truth About Vibe Coding: AI Won’t Save You From Debugging


This content originally appeared on DEV Community and was authored by Mikael Santilio

In recent months, the term vibe coding has gained traction — generating code with the help of AI, iterating quickly until you get something that works.

Does it work? Yes, especially for problems that are widely documented on the internet. But like any powerful tool, it must be used consciously — especially in a corporate environment, where the challenges are very different from GitHub tutorials.

This article walks you through a debugging process you can apply to ensure that the code generated (by you, AI, or colleagues) actually works. It also covers where AI shines and where it tends to fail.

1. Where AI Works Well — and Where It Fails

AI is great at solving problems that have already been solved and are widely documented. If you need to create a CRUD with JWT authentication, craft a complex SQL query, or set up an Express server, AI will likely get it right quickly. That’s because the model has seen many similar examples during its training.

But internal company projects are a different story. Here, the codebase is closed, with unique business rules, custom architecture, and specific integrations — things the AI has never seen. In these cases, asking it to solve a complex problem end‑to‑end rarely works.

What works better:
Break big problems into smaller, well‑defined tasks, such as:

  • Creating utility functions
  • Suggesting regex patterns for validation
  • Building data migration scripts
  • Writing unit tests

2. Debugging Process

When code — whether written by you, others, or AI — misbehaves, follow this simple but effective process:

  • Check what the error is returning

    • Read the stack trace carefully to understand where the problem occurred.
    • Example: 
     NoMethodError: undefined method `map' for nil:NilClass
     from user_service.rb:42:in `get_user_list'

    Here, you can already tell that .map is being called on something that is not an array, likely nil.

Note: If the application does not store stack traces in logs and you cannot see them easily, you will need to add breakpoints or debug instructions (puts, logger.info, etc.) throughout the flow until you pinpoint exactly where the problem happens.

  • Reproduce the error in a controlled environment

    • Set up a local replica of the project or use an isolated environment (container, VM, etc.).
    • In the example above, create a quick test calling get_user_list with the same parameters used in the environment where the error occurred.
    • This confirms whether the bug is a real code issue or just an environment/configuration quirk.

  • Trace the error path

    • If the bug is in an endpoint, place strategic breakpoints along that flow to inspect variables.
    • In the example, adding a binding.irb right before the .map call revealed that fetch_users_from_db was returning nil instead of an empty array.
    • Fixing it: 
     def get_user_list
   users = fetch_users_from_db || []
   users.map { |u| format_user(u) }
 end

    removes the error.

Logic Errors

Not all problems throw exceptions. A logic error occurs when code runs without crashing but produces incorrect results.
This can happen, for example:

  • Incorrect database values
    The system stores "Brazil" in the country field when the PRD specifies it should store the ISO code "BR".

  • Wrong business rule applied
    A 15% discount being applied when the requirement was 10%, due to an incorrect backend calculation.

  • Incomplete or reversed workflow
    An API writes to the database before checking permissions, when it should validate first.

These are trickier because they leave no clear traces in the stack trace — you must trace the flow step‑by‑step, inspect variables, analyze SQL queries, and compare the real output against what the PRD defines as correct.

3. The Risk of Context Rot

Just like a human brain can experience brain rot from endless consumption of low‑value content, an AI conversation can suffer from context rot when overloaded with irrelevant information, outdated assumptions, or excessive detail.

In both cases, the “signal” gets buried under too much “noise”, and the output quality drops.

It happens when the AI’s context becomes:

  • Overly long
  • Cluttered with noise
  • Outdated compared to the current problem state

The result? Inaccurate answers or focus on the wrong aspects.

How to avoid it:

  • Periodically summarize the conversation
  • Restart the prompt with a clean, concise summary
  • Avoid dumping large, unfiltered logs

4. Security When Using AI for Development

Regardless of the problem:

  • Never share credentials or sensitive data.
  • Do not send private integration code without anonymizing it.
  • For database access, avoid write permissions when running AI‑generated code unless you’ve audited it.

5. AI as Co‑Pilot, Not Pilot

Using AI to generate code isn’t wrong. But remember:

  • For public and well‑known problems, it can create fast, high‑quality solutions.
  • For internal and complex projects, use it for smaller tasks and always review the output.
  • The developer’s role is to analyze, validate, and debug — that’s what ensures AI‑generated code is truly useful in real‑world environments.

Conclusion

The vibe coding hype doesn’t replace solid knowledge. AI is powerful but works best when guided clearly and used for well‑defined tasks.

In the end, knowing how to debug remains one of the most valuable skills a developer can have — and it’s what makes AI‑generated code truly useful in the real world.

Sources


This content originally appeared on DEV Community and was authored by Mikael Santilio