Bug Hunter (Ranked Causes + Minimal Fixes)

This prompt approaches debugging systematically by ranking likely causes and proposing minimal, targeted fixes. Unlike generic debugging advice, it demands precision: exact line numbers, quoted code sections, and changes that address the specific error without unnecessary refactoring. The output provides both immediate fixes and optional improvements, clearly separated.

GPT / Claude2 variables

Prompt

You are a debugging assistant. Be precise, not dramatic.

Input:
CODE: {CODE}
ERROR: {ERROR}

Rules:
- Ask at most 2 clarifying questions only if you truly cannot proceed.
- Prefer minimal fixes first. Refactor is optional and must be separated.
- If you cite a line number, quote that exact line/section from CODE.

Output format:
A) Quick read (1–2 sentences): what the error *likely* means.
B) Likely causes (ranked 1–6):
   For each cause:
   - Where: file/line/area
   - Why it fails (short)
   - Smallest fix (exact change)
C) Patch snippet(s) (only the changed lines)
D) Quick test plan (3 checks)
E) Optional cleanup refactor (if it improves clarity without changing behavior)

Quick brief

Purpose

Debug code without random guessing disguised as confidence.

Expected output

A structured debugging report containing: a quick interpretation of what the error means, six ranked potential causes with exact locations and minimal fixes, patch snippets showing only changed lines, a three-point test plan to verify the fix, and clearly labeled optional refactoring suggestions that improve code quality without changing behavior.

Customize before copying

Replace these placeholders with your own context before you run the prompt.

{CODE}{ERROR}

Works well with

GPT

Claude

Variations

Also propose a cleaner refactor after the minimal fix (clearly labeled).

Add minimal logging suggestions to confirm the hypothesis.

Explain the bug like I'm new to the language, but keep the fixes the same.

Prioritize root-cause over symptom-fix (still keep minimal patch).

What this prompt helps you do

When to use it

Deploy this when facing cryptic error messages, unexpected behavior in working code, performance issues with unclear origins, or when you've tried obvious fixes without success. Most valuable when you need to ship a fix quickly but want to understand the underlying issue.

How it works

The prompt forces a structured diagnostic process: interpreting the error message, identifying potential causes ranked by likelihood, proposing minimal fixes for each, providing exact code changes, and outlining verification steps. The separation of minimal fixes from optional refactors prevents scope creep during critical debugging.

Best practices

Include the complete error stack trace and relevant code context, not just the error message. Be specific about when the error occurs—on startup, after certain actions, or under specific conditions. If you have suspicions about the cause, mention them to help the AI prioritize hypotheses. Always test the minimal fix before implementing the optional refactor.

Common mistakes

Providing incomplete code snippets that lack necessary context. Skipping straight to the refactor suggestions without testing the minimal fix first. Not specifying the programming language, framework version, or runtime environment when these could affect the diagnosis. Accepting the first suggested fix without understanding why it works.

What you should expect back

Limitations

Requires you to provide the actual code and error message—it can't debug from vague descriptions. Less effective for race conditions, timing issues, or bugs that depend on external system state. The AI can't run your code, so complex interactions or environment-specific issues may need human verification.

Model notes

Works best with GPT-4 and Claude for code-heavy tasks. Provide code in markdown code blocks with language specified for proper syntax highlighting. Include line numbers if referencing specific locations. Works with any programming language but is most accurate with popular languages like Python, JavaScript, TypeScript, Java, and Go.

Real-world applications

Junior developers use this to learn debugging methodology while fixing issues. Senior engineers use it to quickly triage unfamiliar codebases. Teams use it during incident response to generate fix hypotheses. Code reviewers use it to understand why certain changes were made.

How to tell if it worked

Success means getting a working fix with the minimal patch, understanding why the bug occurred, and having confidence the fix doesn't introduce new issues. The test plan should catch regressions. If the fix works but you don't understand why, ask follow-up questions before moving on.

Where to go next

Use after SQL Query Builder if debugging database-related errors. Combine with Rewrite for Clarity to document complex fixes for your team. Follow up with the optional refactor using Compare and Pick if you're evaluating multiple improvement approaches.

Appears in collections

Prompt Pack: The Ones You'll Actually Reuse

A small set of prompts for better output quality in real workflows: debugging, rewriting, and writing honestly.

Developer AI Stack (Lite, Practical)

A focused set of tools and prompts that reduce time-to-fix and time-to-ship, without turning your brain into a checklist.

Debugging Workflow (Systematic, Not Random)

Stop guessing. Find the bug, fix it properly, and prevent it from coming back.

API Documentation Complete (Devs Can Actually Use It)

Document APIs so developers succeed on first try, not third support ticket.

Code Review System (Helpful Feedback, Not Nitpicks)

Review code that improves quality, teaches developers, and ships faster.

Learning New Programming Language (Practical, Not Academic)