ChatGPT as Your Algorithm Tech Lead: 12 Battle-Tested Prompts for LeetCode, Olympiads, and FAANG Interviews
Most people use ChatGPT for algorithms the same way they use a calculator to check 2+2: they paste a problem, get a solution, copy-paste, and move on. Three months later they still can't solve a two-pointer problem under time pressure. The problem isn't the model — it's the prompt. A vague "solve this" gives you a solution you'll forget by Tuesday.
A well-constructed prompt turns the same model into something closer to a patient tech lead: it forces constraints, asks you to think first, generates edge cases you'd never consider at 1 a.m., and simulates the pressure of a real interview loop. The difference between "explain this" and "before giving the solution, ask me three questions that would reveal whether I understand the invariant" is enormous.
Below are 12 prompts I actually keep in a text file and reuse. Each one includes the exact wording, why it works, and a concrete example. They're ordered roughly from "learning a pattern" to "surviving a mock FAANG loop." Sources and terminology follow real, verifiable references — from CLRS (Cormen et al., Introduction to Algorithms, 4th ed., MIT Press, 2022) to the official LeetCode and Codeforces documentation.
1. The Pattern-First Explainer (not the Solution-Giver)
When to use: Day one of a new topic — sliding window, monotonic stack, union-find. You want to understand the shape of problems, not memorize one answer.
The prompt:
You are an algorithms mentor. I'm learning the [PATTERN] pattern.
Do NOT give me a full solution. Instead:
1. Explain the invariant that makes this pattern correct.
2. Give me 3 problems where it applies, ordered by difficulty.
3. For each, tell me the trigger phrase in the problem statement that signals this pattern.
4. End with one counterexample: a problem that LOOKS like it fits but doesn't, and why.
Example: Replace [PATTERN] with "sliding window with variable size." A good answer will mention the invariant "the window always contains a valid subarray," trigger phrases like "longest substring without repeating," and the counterexample of "maximum subarray sum" — which looks like a window problem but is solved by Kadane's algorithm in O(n) with a different invariant.
Why it works: It refuses the dopamine hit of a finished solution and forces pattern recognition, which is what actually transfers to unseen problems.
2. The Complexity Autopsy
When to use: You have a working solution but suspect it's not optimal, or you can't explain why it's O(n log n).
The prompt:
Here is my solution:
[PASTE CODE]
Do the following:
1. Derive the time and space complexity rigorously, line by line. Show the summations.
2. Identify the dominant term and explain why the others are dropped.
3. State the theoretical lower bound for this problem and cite the argument (e.g., comparison-based sorting needs Omega(n log n)).
4. If my solution is not optimal, name the better approach — but do NOT write the code yet.
Example: For a naive O(n²) two-sum using nested loops, the model derives the sum Σ_{i=1}^{n}(n-i) = n(n-1)/2 = O(n²), then points out that a hash map gives O(n) expected time. It won't hand you the code until you ask — which keeps you in the driver's seat.
Why it works: Complexity analysis is where interviews are won or lost. Forcing the derivation line by line exposes the hand-waving you'd otherwise get away with.
3. The Failing-Input Generator
When to use: Your code passes the provided examples but you have a nagging feeling about edge cases.
The prompt:
Here is my function and its problem statement:
[PASTE]
Act as an adversarial tester. Generate 8 test cases designed to BREAK my code:
- empty input, single element, all-identical elements
- maximum allowed input size
- negative numbers, zeros, integer overflow boundaries (2^31-1, -2^31)
- inputs that trigger worst-case behavior in my data structure
For each, state the expected output and WHY it might fail.
Example: For a binary search on a rotated sorted array, this prompt reliably surfaces the case where the array has duplicates ([1,1,1,3,1]), the case where the target is at the pivot, and the empty-array case. All three are classic interview traps.
Why it works: LLMs are notoriously optimistic about their own code. Framing the task as "break this" flips the objective and produces genuinely nasty inputs.
4. The Invariant Whitener
When to use: You wrote a loop that works but you can't articulate why in an interview. Interviewers at FAANG-level companies (and in competitive programming editorials) expect a loop invariant.
The prompt:
Here is my loop-based solution:
[PASTE]
Write a formal loop invariant for the main loop. Structure it as:
- Initialization: the invariant holds before the first iteration.
- Maintenance: if it holds before an iteration, it holds after.
- Termination: when the loop ends, the invariant gives us the useful property.
This is the standard CLRS proof structure. Be precise about indices.
Example: For insertion sort, the invariant is "at the start of each iteration of the outer for loop, the subarray A[1..j-1] consists of the elements originally in A[1..j-1] but in sorted order" — straight from CLRS Chapter 2. Having the model phrase this for your code is a fast way to internalize the discipline.
Why it works: It maps directly onto a known, citable proof framework, so the output is verifiable rather than vibes-based.
5. The Test-Case Factory (Brute Force as Oracle)
When to use: You have an optimized solution and want to stress-test it against a slow but obviously-correct version.
The prompt:
Problem: [PASTE]
My optimized solution: [PASTE]
1. Write the simplest possible brute-force solution (even O(2^n) is fine).
2. Write a Python random-test harness that:
- generates random small inputs,
- runs both solutions,
- compares outputs and prints the first mismatch.
3. Suggest the input size range where brute force is still feasible.
Example: For a DP problem on a string of length up to 20, the brute force can enumerate all subsequences (2^20 ≈ 1M, fine). The harness runs 1000 random cases in seconds and usually finds the bug on the first try — far faster than staring at code.
Why it works: This is exactly the "stress testing" methodology documented on Codeforces and used in competitive programming. The brute force acts as a correctness oracle.
6. The Hint Ladder (Anti-Spoiler Mode)
When to use: You're stuck on a LeetCode problem and don't want the full solution dumped on you.
The prompt:
I'm stuck on this problem: [PASTE STATEMENT]
Give me hints ONE AT A TIME, from vaguest to most specific, like a Socratic tutor:
Level 1: a question about what data structure might help.
Level 2: a nudge toward the key observation.
Level 3: the algorithm in words, no code.
Level 4: pseudocode.
Level 5: full code with comments.
Start at Level 1. Wait for me to say "next" before advancing.
Example: On "Trapping Rain Water," Level 1 asks "what information do you need at each index to know how much water sits there?" Level 2 hints at precomputing prefix/suffix maxima. You'll often solve it yourself at Level 2 — which is the point.
Why it works: It preserves the productive struggle that builds real skill, while still giving you an escape hatch.
7. The Optimal-Complexity Challenger
When to use: You have an accepted solution and want to know if it's actually optimal.
The prompt:
My solution runs in O([X]) time and O([Y]) space.
Problem: [PASTE]
1. Is there a known lower bound for this problem? Cite the argument.
2. Can the time be improved? If not, explain why (e.g., information-theoretic bound).
3. Can the space be reduced to O(1) or O(log n)? Name the technique (e.g., Morris traversal).
4. Give one research paper or textbook section that covers the optimal approach.
Example: For "find the duplicate number" with the constraint of no extra space and O(n) time, this prompt points to Floyd's cycle detection (the tortoise-and-hare algorithm, originally from Floyd, 1967) and explains why the O(1)-space solution works via the pigeonhole principle.
Why it works: It connects your working solution to the theoretical ceiling, which is exactly the level of reasoning senior interviews probe for.
8. The Olympiad-Style Problem Generator
When to use: You're prepping for Codeforces, ICPC, or IOI-style contests and LeetCode's tidy problems feel too easy.
The prompt:
Generate a competitive-programming problem in the style of Codeforces Div. 2 C/D.
Constraints:
- Topic: [e.g., segment tree with lazy propagation]
- Constraints must be stated explicitly (n <= 2*10^5, values <= 10^9).
- Include input/output format, 2 sample tests with explanations, and the intended solution's complexity.
- The problem must require a non-obvious observation; do not make it a direct template.
Example: A request for "prefix sums + binary search" might produce: "Given n queries of the form (l, r, x), count how many subarrays in [l, r] have sum exactly x." The intended solution combines prefix sums with a hash map, O(n log n). It's not a template — you have to see the reduction.
Why it works: Real contest problems test observation, not recall. This prompt forces the model to hide that observation behind a plausible story.
9. The Data-Structure Interviewer
When to use: You want to drill the "design" round — LRU cache, insert-delete-getRandom, min stack — where the interviewer cares about your class design, not just an algorithm.
The prompt:
Act as a staff engineer interviewing me on data-structure design.
Ask me to design [LRU CACHE / MIN STACK / etc.].
Rules:
- Ask about expected time complexity for each operation before I code.
- Push back on my choices; if I pick a hash map, ask what happens under collisions.
- Require me to state the invariant linking my two structures.
- Only after I've described the design in words, ask me to write the code.
Example: For an LRU cache, this quickly surfaces the classic pairing: a hash map (key → node) plus a doubly-linked list for recency. The interviewer prompt will ask why a singly-linked list fails (you can't unlink a node in O(1) without a prev pointer) — a question real interviewers ask.
Why it works: It reproduces the back-and-forth of a design round, where the interviewer probes your reasoning rather than your typing speed.
10. The Behavioral + Algorithm Hybrid Round
When to use: FAANG loops mix coding with behavioral questions. You need to practice explaining trade-offs under time pressure while narrating aloud.
The prompt:
Simulate a 45-minute FAANG interview. Structure:
- Minute 0-5: I'll restate the problem and ask clarifying questions. You play the interviewer and respond.
- Minute 5-35: I'll think aloud. Interrupt me if I go silent for too long or skip edge cases.
- Minute 35-45: I'll analyze complexity and discuss trade-offs. Ask one follow-up about scaling.
Problem: [PASTE]
Start by presenting the problem as an interviewer would.
Example: For "Design a rate limiter," the simulated interviewer will push on whether you mean a fixed-window or sliding-window limiter, ask about distributed coordination, and follow up with "how does this behave at 10x traffic?" — the exact escalation pattern reported in many candidate debriefs.
Why it works: It rehearses the communication dimension that trips up strong coders who stay silent and lose points on collaboration signals.
11. The Post-Mortem Analyzer
When to use: After every failed or slow submission. This is the highest-leverage prompt in the list.
The prompt:
I attempted this problem: [PASTE]
Here is my final (wrong/slow) code: [PASTE]
Here is the editor's tag: [e.g., "dynamic programming, bitmask"].
Do a post-mortem:
1. What pattern did I fail to recognize, and what was the trigger I missed in the statement?
2. What is the smallest conceptual gap between my approach and the intended one?
3. What one drill should I do next to close this gap?
4. Log this as a weakness in the format: [topic
| failure mode | fix].
Example: If you tried a greedy approach on a problem that needed DP, the post-mortem will identify that you ignored the "overlapping subproblems" signal and suggest practicing the "greedy vs. DP decision" drill. Keeping a running log of these is how you convert failures into a curriculum.
Why it works: Reflection is where learning consolidates. Doing it systematically, with a consistent log format, turns scattered practice into a targeted plan.
12. The Mock FAANG Loop Simulator
When to use: Two weeks before a real interview. You want full-loop pressure, not single questions.
The prompt:
Run a full FAANG-style loop for a [LEVEL, e.g., L4/SDE II] role.
Round 1 (45 min): medium LeetCode, focus on [topic].
Round 2 (45 min): hard LeetCode or design.
Round 3 (30 min): behavioral — ask me about a time I disagreed with a teammate.
For each round: present the problem, wait for my response, then give a scoring rubric
(1-4, like Amazon's Leadership Principles scale) with specific feedback.
Do not reveal solutions until I've attempted or given up.
Example: A common round-1 medium for L4 is "Course Schedule II" (topological sort). The rubric will score you on: did you identify the DAG structure, handle the cycle case, and state O(V+E) complexity. Round 3 behavioral scores structure (STAR format) and ownership signals.
Why it works: It bundles the whole experience — technical, design, and behavioral — into one session, which is far closer to the real thing than isolated problem-solving. Amazon's published Leadership Principles and their 1-4 scoring scale are public and widely reported; the model can approximate this structure without inventing specifics.
How to Actually Use This List
Don't run all 12 in a day. A realistic week looks like this: Monday–Tuesday, use prompts 1–3 on a new pattern. Wednesday, prompt 5 to stress-test. Thursday, prompt 6 when stuck, prompt 7 to push for optimality. Friday, prompt 11 as a post-mortem on the week's failures. Saturday, prompt 12 for a full mock. Sunday, rest — your brain consolidates on rest.
The single biggest mistake is using the model as an answer key. Every prompt above is designed to make you do the thinking and the model do the scaffolding. The prompts that ask the model to withhold — the hint ladder, the complexity autopsy, the pattern-first explainer — are the ones that pay off in the interview room, because on the whiteboard there is no model to ask.
If you want to go deeper, the two most citable free resources remain CLRS (for proofs and invariants), the official LeetCode and Codeforces problem archives (for practice), and Floyd's 1967 cycle-detection note (for the O(1)-space pointer technique). Combine those with these prompts, keep a weakness log, and the FAANG loop stops being a lottery and becomes a checklist.
Start with prompt 11 tonight — take the last problem you failed and run the post-mortem. That one habit, more than any other, is what separates people who grind for a year from people who pass in three months.
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