Why do LLMs often describe a bug instead of fixing it?

Vague prompts invite vague answers. This builder gives the model a strict procedure — restate the bug, find the root cause, explain it, return corrected code, list edge cases — so it commits to a concrete fix.

Do I need all the fields?

No. The more context you provide, the better the diagnosis, but the prompt adapts and only includes the sections you fill in. Even just an error message and code snippet produces a useful prompt.

Does it execute or run my code?

No. It builds the prompt locally in your browser with no API key and no network call. You run the resulting prompt in whatever LLM you use.

What if the model can't be certain of the cause?

The prompt instructs it to name the single most useful thing to check and still give a best-effort fix, rather than stalling. That keeps you moving even with incomplete context.

Why include expected versus actual behavior?

It anchors the model on the real symptom. Without it, the model may "fix" code that compiles but still does the wrong thing. Stating expected and actual outcomes targets the actual defect.

What is the Debugging Prompt Builder?

Assembles a debugging prompt from your error message, stack trace, code context, and expected-versus-actual behavior in a structure that pushes LLMs to find the root cause and return a specific, paste-ready fix. It runs free in your browser on Gera Tools, with nothing uploaded.

Debugging Prompt Builder

Name: Debugging Prompt Builder
Creator: Gera Tools
License: https://creativecommons.org/licenses/by/4.0/

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Debugging prompt builder

Ask an LLM to “look at this error” and you often get a paragraph explaining what the error means — not a fix. The debugging prompt builder structures your error message, stack trace, code, and expected-versus-actual behavior into a prompt that forces the model through a real debugging procedure and returns a specific, paste-ready fix.

How it works

You pick the language and fill in whatever context you have: the error message, the stack trace, the relevant code, and a short statement of what you expected versus what happened. The tool assembles a prompt that tells the model to work in order — restate the bug, pinpoint the root cause with line references, explain why it produces the symptom, provide corrected code ready to paste, and list the edge cases the fix now covers and how to verify it. Empty fields are omitted automatically. It all runs locally; no API key is required.

Why the structured procedure matters

The default LLM behavior when given an error is to pattern-match it to common causes and explain those causes. This is useful for understanding but not for debugging — most real bugs are specific to your code and context, not to the general category of error. By forcing the model to work through a sequence of steps tied to your actual stack trace and code, the prompt reduces the chance of a plausible-but-wrong generic explanation.

The explicit “root cause at line X” step is especially important. Without it, models often give a hypothesis in prose form that sounds confident but could apply to dozens of different issues. Requiring a line number forces the model to commit to a specific location and defend that choice.

A worked example

Error message: TypeError: Cannot read properties of undefined (reading 'map')

Expected behavior: The page should render a list of search results.

Actual behavior: The page crashes on load after fetching results.

In this case, the structured prompt would guide the model to identify that results is undefined at the point .map() is called — most likely because the API response hasn’t resolved yet, or because the response shape changed and results is no longer a top-level property. The model should then provide corrected code with a null check or optional chaining guard, not just describe what undefined means.

Tips and notes

Include the full stack trace. The top frame is the symptom; the frames below it usually point at the cause.
State expected versus actual. This is what separates “fixed the crash” from “fixed the behavior” — give the model the real target.
Trim the code to what matters. Paste the function plus anything it calls that could be implicated, not the whole file.
Use it to write a failing test next. Once the model names the root cause, pair this with the test-generation builder to lock the fix in place.
For intermittent bugs, describe the conditions under which it occurs (high load, specific user input, a particular browser) — this context changes the diagnostic hypothesis significantly.