Introduction
Debugging is an inevitable and often time-consuming part of software development. According to a 2020 study by the University of Cambridge, developers spend nearly 50% of their working time debugging. Yet, many engineers still rely on manual print statements or trial-and-error approaches. With the rise of large language models (LLMs) like GPT-4, Claude, and Gemini, you now have a powerful ally: well-crafted prompts that can analyze logs, explain errors, suggest fixes, and even refactor buggy code. This article provides 15 ready-to-use prompts for debugging, each with a specific use case, an explanation, and a practical example. Use them as a cheat sheet to slash your debugging time.
Why Prompts Work for Debugging
LLMs are trained on vast corpora of code, error messages, and debugging discussions (including Stack Overflow, GitHub issues, and official documentation). When you give a clear, context-rich prompt, the model can:
- Identify the root cause of an error from a stack trace.
- Suggest edge cases you might have missed.
- Generate test cases to reproduce the bug.
- Explain complex language-specific behaviors (e.g., JavaScript closures, Python reference semantics).
The key is specificity: vague prompts yield vague answers. The prompts below are designed to be copy-paste ready; just replace placeholders like [language] or [error message].
Prompt 1: Explain an Error Message
Task: You got an error message you don’t understand. You want a plain-English explanation and likely causes.
Prompt:
I am working in [language/framework] and encountered the following error:
[error message / stack trace]
Explain what this error means in simple terms, and list the three most common causes for it. For each cause, provide a one-sentence fix.
Usage example:
I am working in Python and encountered the following error:
TypeError: 'int' object is not callable
Explain what this error means in simple terms, and list the three most common causes for it. For each cause, provide a one-sentence fix.
Expected output: The model explains that you tried to call an integer as a function, and lists causes like shadowing a built-in name, missing operator, or reassigning sum.
Prompt 2: Debug a Log File
Task: You have a large log file and want to find anomalies or patterns.
Prompt:
Here is a log file from my [application/service] containing recent errors. Please:
1. Identify all unique error types.
2. For each type, count the occurrences.
3. Suggest the most likely root cause for the most frequent error.
Log file:
[paste logs]
Usage example:
Here is a log file from my Node.js backend containing recent errors. Please:
1. Identify all unique error types.
2. For each type, count the occurrences.
3. Suggest the most likely root cause for the most frequent error.
Log file:
[2026-07-14 10:12:33] ERROR: Cannot read property 'id' of undefined
[2026-07-14 10:12:34] ERROR: Cannot read property 'id' of undefined
[2026-07-14 10:13:01] WARN: Timeout fetching user data
...
Why it works: The model performs basic log aggregation and pattern recognition, saving you from manual scrolling.
Prompt 3: Find Race Conditions in Concurrent Code
Task: You suspect a race condition in multi-threaded or async code.
Prompt:
Analyze the following [language] code for potential race conditions. The code runs in a multi-threaded environment. For each suspected race, explain:
- The shared resource
- The conflicting operations
- A suggested fix (e.g., mutex, atomic operation, or channel)
Code:
[code snippet]
Usage example:
Analyze the following Python code for potential race conditions. The code runs with multiple threads using concurrent.futures. For each suspected race, explain the shared resource, the conflicting operations, and a suggested fix.
Code:
counter = 0
def increment():
global counter
for _ in range(1000):
counter += 1
Expected output: The model identifies counter as a shared mutable resource and suggests using threading.Lock.
Prompt 4: Convert Error-Prone Code to Safer Patterns
Task: You have working code that is brittle or unsafe (e.g., uses eval, raw SQL, or mutable defaults).
Prompt:
Refactor the following [language] code to eliminate common pitfalls and improve safety. Specifically:
- Replace any unsafe operations with safer alternatives.
- Add input validation.
- Use idiomatic error handling.
- Explain each change.
Code:
[code snippet]
Usage example:
Refactor the following Python code to eliminate common pitfalls and improve safety. Specifically: replace any unsafe operations with safer alternatives, add input validation, use idiomatic error handling, and explain each change.
Code:
def get_user_input():
return eval(input("Enter expression: "))
Expected output: The model replaces eval with a safe parser or type conversion, and explains the security risk.
Prompt 5: Reproduce a Bug with a Minimal Test Case
Task: You have a bug report but no clear reproduction steps.
Prompt:
Given the following bug description, write a minimal, self-contained test case in [language] that reproduces the bug. Include only the necessary code and assertions to demonstrate the failure.
Bug description:
[description]
Usage example:
Given the following bug description, write a minimal, self-contained test case in JavaScript that reproduces the bug. Include only the necessary code and assertions to demonstrate the failure.
Bug description:
When I call Array.sort() on an array of numbers, the order is incorrect. For example, [10, 2, 1].sort() gives [1, 10, 2] instead of [1, 2, 10].
Expected output: The model writes a test showing the default lexicographic sort and suggests using a comparator.
Prompt 6: Compare Two Code Versions for Regression
Task: A feature that used to work now fails after a recent commit.
Prompt:
Here are two versions of the same [language] function. Version A works correctly, Version B does not. Identify the exact differences that cause the bug in Version B, and explain why each change leads to the failure.
Version A (working):
[code]
Version B (broken):
[code]
Usage example: Use this after a git diff reveals suspicious changes.
Prompt 7: Performance Debugging – Find Bottlenecks
Task: Your code is slow, and you want to identify inefficient parts.
Prompt:
Analyze the following code for performance issues. Suggest specific optimizations, including:
- Time complexity improvements
- Unnecessary loops or allocations
- Better data structures
- Caching opportunities
Code:
[code snippet]
Usage example: A nested loop over large arrays might be replaced with a hash map lookup.
Prompt 8: Debug a Database Query or ORM Issue
Task: A query returns wrong results or is slow.
Prompt:
I am using [ORM name] with [database type]. The following query is supposed to [intended result], but it returns [actual result]. Explain why and provide a corrected query.
Query:
[code]
Prompt 9: Explain a Regex Pattern
Task: You inherited a complex regex and don’t understand what it matches.
Prompt:
Explain the following regular expression in plain English, and give three examples of strings that match and three that do not.
Regex: /^(?=.*[A-Z])(?=.*\d).{8,}$/
Prompt 10: Memory Leak Detection
Task: Your application’s memory usage grows over time.
Prompt:
Review the following [language] code for potential memory leaks. Look for:
- Unclosed resources (files, sockets, connections)
- Retained references in closures or listeners
- Improper use of caches
- Circular references in garbage-collected languages
Code:
[code snippet]
Prompt 11: Debug Async/Await Issues
Task: Promises are not resolving in the expected order, or you get unhandled rejections.
Prompt:
Identify problems in the following async code. Focus on:
- Missing await
- Unhandled promise rejections
- Improper error propagation
- Race conditions between async operations
Code:
[code]
Prompt 12: Convert Error Messages to User-Friendly Notifications
Task: Your app shows raw errors to users. You want to map them to human-readable messages.
Prompt:
Given the following list of common error codes/messages from [system], create a mapping table with:
- Original error code/message
- User-friendly message (max 50 chars)
- Suggested next action for the user
Error list:
[list]
Prompt 13: Integrate a Third-Party API Debugging
Task: You are calling an external API (e.g., Stripe, Google Analytics, Telegram) and getting unexpected responses or errors.
Prompt:
I am integrating [API name] and encountering the following issue:
- Request I send: [request details]
- Response I receive: [response/error]
- Expected behavior: [what should happen]
Diagnose the problem and provide a corrected request or error handling.
Note: ASI Biont supports integration with many third-party services, including the ones mentioned above, via API — see the courses at asibiont.com/courses for detailed guides.
Prompt 14: Debugging CSS/UI Rendering Bugs
Task: A layout works in one browser but breaks in another.
Prompt:
The following CSS works correctly in Chrome but breaks in Safari. Identify the compatibility issue and provide a cross-browser fix.
CSS:
[code]
Browser versions: [versions]
Prompt 15: Generate Unit Tests to Prevent Future Bugs
Task: You fixed a bug and want to ensure it never returns.
Prompt:
Write unit tests in [testing framework] for the following function. Cover:
- Normal cases
- Edge cases (empty input, null, large values)
- The specific bug scenario we fixed: [description of bug]
Function:
[code]
Conclusion
Debugging is not just about fixing errors — it’s about understanding systems deeply. The prompts above act as a force multiplier, turning your AI assistant into a junior debugger, a code reviewer, and a documentation writer rolled into one. To get the best results:
- Be specific — include language, framework, and full error messages.
- Provide context — share the surrounding code or configuration.
- Iterate — if the first answer isn’t perfect, refine your prompt.
Whether you’re a beginner or a senior developer, these prompts will help you move from “why is this broken?” to “here’s the fix” in minutes. Bookmark this list, and happy debugging!
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