ChatGPT Prompts for Coding Interviews — 20+ Developer Prompts

A practical set of ChatGPT prompts for coding interview preparation — from data structures and system design to mock interviews and salary negotiation.

How to use AI for coding interview prep the right way

Using ChatGPT for interview prep works — if you use it as a coach, not a cheat sheet. The difference is in how you prompt. Ask for hints and explanations instead of full solutions, and you will build the problem-solving skills interviewers actually test. Ask for ready-made answers and you will freeze when the interviewer changes one detail.

The prompts below are built on that principle. Each one turns ChatGPT into a specific interview role — a DSA tutor, a system-design interviewer, a code reviewer, a mock interviewer — so you practise the same way you will be tested. All of them work on ChatGPT, Claude, and Gemini, and you can run them through the free Advanced Prompt Optimizer to tighten the wording before you start.

Data structures and algorithms practice prompts

1) "You are a senior DSA interviewer at a top tech company. Give me one medium-difficulty problem about {topic: arrays, graphs, dynamic programming}. Do not show the solution — first ask me to explain my approach, then give me hints one at a time." 2) "I am practising {topic} on LeetCode. Analyse my solution below and tell me the time and space complexity, then suggest one optimisation without rewriting the code. My solution: {paste code}" 3) "Give me 5 variations of this problem with increasing difficulty: {paste problem}. For each, tell me what concept it tests and what changes in the approach."

4) "Act as a strict interviewer. Give me a 45-minute DSA question, wait for my solution, and score my answer on correctness, efficiency, and communication — with a short feedback paragraph." 5) "Explain {concept: recursion, memoization, sliding window} like I am a junior developer, with one real-world analogy and one small example."

Run your final practice prompts through the Prompt Debugger before a session — it scores prompt quality out of 100 and flags missing details, so your AI coach gives you exactly the kind of feedback you need.

System design interview prompts

System design interviews are about structure, not memorised facts. These prompts keep you in the driver's seat: 1) "You are a staff engineer interviewing me for a senior role. Ask me to design {service: a URL shortener, a chat app, a ride-sharing backend}. Guide me through requirements, scale estimation, data model, API design, and bottlenecks — one step at a time." 2) "I described my design for {service}. Here is my outline: {paste}. Act as a skeptical architect and list the 3 weakest points, with why each one matters."

3) "Create a 10-question system design checklist for {service} — covering availability, latency, consistency, and cost — and score my answers from 1 to 5." 4) "Compare {SQL vs NoSQL, queue vs pub-sub, cache strategies} for my design in a table: when to use each, trade-offs, and a real example."

Keep your design notes in a reusable chain: use the Prompt Chain Builder to build a 3-step workflow — step 1 generates the design question, step 2 reviews your outline, step 3 scores your final answer. Reuse the same chain for every practice session.

Code review and take-home assignment prompts

Take-home tasks and code reviews are where AI help is most useful — and most abused. Use it to understand problems, not to generate the whole submission: 1) "Review my code for {task}. Focus on correctness bugs, edge cases, and readability. List issues by severity with a one-line fix suggestion each. Code: {paste}" 2) "I wrote this solution for {problem}. Before showing me your version, tell me: what edge cases did I miss, and what would a senior engineer ask about my approach?"

3) "Explain the test cases I should write for this function and why: {paste code}" 4) "My take-home asks for {requirements}. Break it into a checklist of deliverables and tell me what part interviewers care about most."

After you fix the issues it flags, run the improved version through the Token Estimator to keep your submission prompt-sized and professional — no interviewer wants a 3,000-token code dump attached to a question.

Mock interviews and behavioural prompts

The behavioural round is 30% of most interview scores, and AI is a patient practice partner: 1) "Act as an interviewer. Ask me behavioural questions for a {role} position — one at a time. After each answer, score it on STAR structure (Situation, Task, Action, Result) and suggest one improvement." 2) "Here is my answer to 'Tell me about a time you failed': {paste}. Rewrite it using the STAR format without changing the facts."

3) "Give me 10 likely questions about my resume: {paste resume highlights} — and after I answer each one, tell me what follow-up the interviewer would ask next."

Before the real interview, practise with a time limit: ask your AI coach to "time each answer at 90 seconds and tell me when I am rambling". Structured practice like this is exactly why the Advanced Prompt Optimizer adds role, format, and constraints — the same pattern that makes interview prompts work. On the day before your interview, run your final practice prompts through the Prompt Optimizer and organise the session with the Prompt Chain Builder.