12 Prompts for Debugging and Finding Bugs in Code

Debugging is often the most time‑consuming part of development. A well‑crafted prompt can turn a vague error into a clear fix. This guide presents 12 ready‑to‑use prompts that help you analyze error messages, review logic, inspect logs, and validate edge cases. Each prompt includes a real‑world scenario and a code example so you can apply it immediately.

1. Explain an Error Message

Prompt: "Explain the following error in simple terms. What is the most likely root cause? Provide a fix and a preventive measure."
Example: A Python KeyError in a dictionary lookup.
Code:

# Bug: Missing key
data = {"name": "Alice"}
print(data["age"])  # KeyError

AI Response: The error occurs because "age" does not exist. Fix: use .get("age", default) or check with if "age" in data. Prevent: always validate dictionary keys before access.

2. Review Code for Logic Errors

Prompt: "Review this function for logical mistakes. Assume the input is always a positive integer. Show me the corrected version."
Example: A function to compute factorial that incorrectly handles zero.
Code:

def factorial(n):
    if n == 0:
        return 0  # Bug: should be 1
    return n * factorial(n-1)

AI Response: The base case is wrong. factorial(0) must return 1. Correct function: if n == 0: return 1.

3. Compare Expected vs Actual Output

Prompt: "Here is my code, the expected output, and the actual output. Find the discrepancy and suggest a fix."
Example: A sorting algorithm that should produce ascending order but returns descending.
Code:

def my_sort(arr):
    for i in range(len(arr)):
        for j in range(i+1, len(arr)):
            if arr[i] < arr[j]:  # Bug: wrong comparator
                arr[i], arr[j] = arr[j], arr[i]
    return arr

AI Response: The condition arr[i] < arr[j] causes larger values to move left. Change to arr[i] > arr[j].

4. Analyze Logs and Find Root Cause

Prompt: "I have these logs from a production incident. Identify the first anomaly and trace the chain of events that led to the error."
Example: Logs showing database timeout after a spike in traffic.
AI Response: The root cause is a connection pool exhaustion caused by slow queries. Solution: add connection pooling limits, optimize slow query, implement retries with backoff.

5. Refactor Code to Improve Debuggability

Prompt: "Refactor this code to make it easier to debug. Add clear variable names, separate concerns, and include logging at key points."
Example: A monolithic function that processes user uploads.
Code:

def process(data):
    # many lines of mixed logic
    pass

AI Response: Break into smaller functions: validate_input, transform_data, save_to_db. Add loguru or print statements before each step.

6. Generate Unit Tests That Catch the Bug

Prompt: "Generate unit tests for the following code. Include edge cases like empty input, negative numbers, and large values. Each test should have an assertion."
Example: A function that calculates average of a list.
Code:

def average(lst):
    return sum(lst) / len(lst) if lst else 0

AI Response: Tests for empty list (returns 0), one element, negative numbers, and large floats. Use pytest.

7. Simulate Edge Cases

Prompt: "List all possible edge cases for this code snippet. For each edge case, show what happens with the current implementation and whether it is handled correctly."
Example: A function that divides two numbers.
Code:

def divide(a, b):
    return a / b

AI Response: Edge cases: b=0 (ZeroDivisionError), negative b, very large a/b, both zero. Current code fails on division by zero. Add if b == 0: return None or raise custom exception.

8. Suggest Debug Print Statements

Prompt: "Insert strategic print or log statements in this code to help trace the flow and variable values at each step. Use a consistent format like [DEBUG] variable_name = value."
Example: A recursive binary search.
AI Response: Add prints before the recursive calls to show mid, arr[mid], and the selected half. Also print the result when returning.

9. Identify Concurrency / Race Conditions

Prompt: "Examine this multithreaded code for potential race conditions. Suggest synchronization mechanisms (locks, semaphores, atomic operations)."
Example: Two threads incrementing a shared counter without a lock.
Code:

counter = 0
def increment():
    global counter
    for _ in range(100000):
        counter += 1

AI Response: Race condition because counter += 1 is not atomic. Use threading.Lock() or queue.Queue.

10. Detect Memory Leaks

Prompt: "Review this code for potential memory leaks. Look for unclosed resources, circular references, or unbounded caches. Provide a fix."
Example: A class that opens file handles without closing them.
Code:

class LogWriter:
    def __init__(self, path):
        self.file = open(path, 'w')
    def write(self, msg):
        self.file.write(msg)

AI Response: File is never closed. Use a context manager (with open(...) as f) or implement __enter__ and __exit__.

11. Validate Input / Output Contracts

Prompt: "Add input validation and output assertions to this function. Use type hints and raise clear exceptions on invalid inputs."
Example: A function that computes square root.
Code:

def sqrt(x):
    return x ** 0.5

AI Response: Check x >= 0, raise ValueError if negative. For x == 0 return 0. For x very small, use math.isclose.

12. Ask "What Could Go Wrong?"

Prompt: "Act as a code reviewer with a security and robustness focus. List every possible failure mode of this function and suggest defense mechanisms."
Example: A function that sends an HTTP request.
AI Response: Failures: network timeout, DNS failure, invalid URL, non‑200 status, large response body, redirect loop. Mitigations: timeout parameter, retry logic, status code check, stream response, follow redirects limit.

Summary Table

Conclusion

Copy these prompts directly into your AI assistant (ChatGPT, Claude, etc.) to save hours of manual debugging. Start with the error‑explanation prompt when you hit an unknown error, and run the edge‑case prompt before merging any new code. The more specific you are in your prompt (including exact error messages, code snippets, and expected behavior), the better the AI can help. Bookmark this article and use it as your debugging co‑pilot.