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Best prompts for ChatGPT for coding using debugging complex algorithms

12 practical, copy-ready prompts to debug, diagnose, and fix complex algorithmic and systems bugs across languages (C/C++, Java, Python). Each entry includes a concise title, explanation, a ready-to-use prompt, a realistic example, and recommended AIs.

GPT-5
Claude Opus 4
Claude Sonnet 4
Gemini 2.5 Pro
Gemini 2.5 Flash
You know that sinking feeling when your code compiles perfectly but crashes mysteriously in production, or when your algorithm works fine on small inputs but crawls to a halt on real data? We've all been there, staring at screens at 2 AM, wondering if ChatGPT can actually help debug these nightmare scenarios or just give us generic advice that misses the mark entirely.
These 12 battle-tested prompts transform ChatGPT into your personal debugging expert, equipped to tackle everything from sneaky off-by-one errors and memory leaks to race conditions and algorithmic bottlenecks across C/C++, Java, and Python. Each prompt comes with real-world examples, exact fixes, and comprehensive test cases that you can copy and paste directly into your AI chat. Instead of wrestling with cryptic error messages and Stack Overflow rabbit holes, you'll get precise diagnoses and working solutions that actually solve your complex algorithmic challenges.
1
Fix off-by-one bug in binary search (C++)
You are a debugging assistant. Analyze the following C++ binary search implementation that intermittently returns wrong indices. Explain the root cause (off-by-one or loop invariant), provide a corrected implementation, a short explanation of why it now works, and at least five unit tests including edge cases. Code: int binary_search(const vector<int>& a, int target) { int l = 0, r = a.size() - 1; while (l <= r) { int m = (l + r) / 2; if (a[m] == target) return m; if (a[m] < target) l = m + 1; else r = m - 1; } return -1; } Observed failure: searching for the first or last element on some inputs returns incorrect index. Explain why and provide a corrected, robust version (handle empty array, integer overflow in mid calculation) and tests.
Identify failing inputs and correct indices/loop invariants, show exact fix and unit tests.
2
Debug dynamic programming memoization error (Python)
You are an expert in dynamic programming debugging. Given the following Python implementation for computing the longest increasing subsequence (LIS) length, find why it returns incorrect answers on some inputs. Explain the bug (wrong memoization key or recursion state), provide a corrected version (iterative or recursive + memo) with complexity analysis, and add unit tests that cover tricky patterns (equal elements, strictly increasing requirement, long sequences). Code: def lis_length(arr): memo = {} def helper(i, prev): if i == len(arr): return 0 key = (i, prev) if key in memo: return memo[key] taken = 0 if prev < arr[i]: taken = 1 + helper(i+1, arr[i]) not_taken = helper(i+1, prev) memo[key] = max(taken, not_taken) return memo[key] return helper(0, float('-inf')) Observed: returns wrong length on arrays with negative numbers and equal elements. Provide a corrected version and 6 unit tests.
Find incorrect state, base case, or memo key leading to wrong DP results, then fix and provide tests.
3
Locate and fix memory leak in C++ loop
You are a C++ memory debugging assistant. Inspect the code below for memory leaks and lifetime issues. Identify where allocations are not freed, show how to reproduce the leak with a small test, provide a fixed version (prefer modern C++ RAII smart pointers or std::vector), and show sample valgrind or sanitizer output before and after the fix. Code: void process(int n) { for (int i = 0; i < n; ++i) { int* buf = new int[1024]; // do some work with buf if (i % 100 == 0) break; // early exit } } Observed: memory usage grows when process is called repeatedly. Explain and fix.
Detect allocation without deallocation; recommend RAII or smart pointers and provide fixed code and valgrind-style checks.
4
Resolve floating-point precision bug in comparisons (Java)
You are a numerical debugging assistant. Given the following Java code that compares doubles and produces incorrect branch decisions, identify precision pitfalls, propose and implement a stable solution (epsilon strategy, relative tolerance, or use BigDecimal where appropriate), and produce unit tests demonstrating correctness. Code: public boolean isZero(double x) { return x == 0.0; } // used in: if (isZero(a/b - 1.0)) { // assume a==b } Observed: occasionally branch not taken due to tiny floating error. Provide corrected implementation, explanation of chosen tolerance, and tests showing edge cases.
Detect unsafe equality comparisons, propose robust numeric tolerances or algorithms, and provide unit tests.
5
Debug race condition in multithreaded counter (Java)
You are a concurrent-systems debugger. Analyze the multithreaded Java code below where a shared counter produces incorrect totals under concurrency. Explain the race, show how to reproduce it with a stress test, provide a thread-safe fix (e.g., AtomicInteger, synchronized, or Lock), compare performance implications, and add a concurrent unit test that reliably demonstrates correctness. Code: public class Counter { private int count = 0; public void inc() { count++; } public int get() { return count; } } Multiple threads call inc() heavily and final count is sometimes less than expected. Fix and test.
Pinpoint data race, demonstrate reproduction, fix with synchronization/atomic ops, and add concurrent tests.
6
Track down segmentation fault from pointer arithmetic (C)
You are a C systems debugging assistant. The following C function sometimes causes a segmentation fault. Explain why (pointer arithmetic or off-by-one), produce a minimized reproducer, fix the code and recommend sanitizer/compilation flags. Provide corrected code and a small test that confirms no segfault. Code: char *concat(const char *a, const char *b) { size_t la = strlen(a); size_t lb = strlen(b); char *s = malloc(la + lb); strcpy(s, a); strcat(s, b); return s; } Observed: segmentation fault on certain inputs. Fix and explain.
Find out-of-bounds pointer use, show minimal reproducer, fix, and add sanitizer flags and test harness.
7
Optimize nested-loop bottleneck into O(n log n) (Python)
You are a performance debugging assistant. The Python function below times out on n=200k due to an O(n^2) nested loop. Propose and implement a more efficient algorithm (aim for O(n log n) or O(n)), explain correctness and complexity, provide micro-benchmarks comparing old vs new implementations, and include test inputs illustrating speedup. Code: def count_pairs(arr): n = len(arr) cnt = 0 for i in range(n): for j in range(i+1, n): if arr[i] + arr[j] == 0: cnt += 1 return cnt Observed: extremely slow for large arrays. Provide optimized solution and tests.
Replace quadratic behavior with an algorithmic improvement (e.g., sort+binary search or heap), provide benchmarks and tests.
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