By skyforbes 
If you’re aiming for Big Tech / FAANG‑style software engineering roles, whether it’s your first job out of university, your second role, or a mid-level engineer targeting senior roles, AI has completely changed how you can prepare.
A few years ago, interview prep meant random LeetCode grinding, reading system design blogs and hoping you guessed the “right” questions. Now you can have an on‑demand “interview coach” in your browser. Tools like ChatGPT, Gemini, and NotebookLM can simulate interviews, explain concepts and organize your notes.
In this guide, I’ll walk through how to use AI tools for interviews effectively, especially for DSA + system design interivews, with concrete prompts, examples in C++/Java, and realistic mock interview flows. I’ll also cover best practices and limitations.
How AI Is Changing Software Engineering Interview Prep
Traditional prep looked like this:
- Solving LeetCode questions alone
- Trying to guess what the interviewer will ask
- Reading system design posts but not getting feedback on your designs
- Hoping your friend has time to do mock interviews
AI flips this:
- You can simulate an interviewer 24/7, for any company level (intern to senior).
- You can get step‑by‑step explanations of dynamic programming, graphs, concurrency, etc.
- You can upload your notes and turn them into a queryable knowledge base using tools like NotebookLM.
Overview of AI Tools for Interview Preparation
ChatGPT: Your Versatile Coding and Design Practice Partner
Best for:
- Coding interview practice (LeetCode‑style, C++/Java)
- Explaining DSA and complexity
- System design mock interviews and feedback
- Behavioral questions and STAR‑style answers
How to use ChatGPT for interview prep:
- Explain concepts you’re weak on. Example prompt:
“Explain the difference between BFS and DFS to me as if I’m preparing for a Google/Meta interview. Include typical Big‑O complexities and one common pitfall for each.”
2. Generate targeted practice problems
“Give me 5 medium‑difficulty array and hash map problems suitable for a FAANG interview. I want to solve them in C++. Don’t show solutions yet.”
3. Simulate a timed coding round
“Act as a technical interviewer for a 45‑minute coding round at a Big Tech company. Ask me one medium and one hard problem. I’ll answer in Java. Do not show me the solution unless I explicitly ask. Track the time and give me feedback at the end.”
4. Get feedback on your solutions. Paste your C++/Java code and ask:
“Review my solution as if you’re an experienced interviewer at Amazon. Comment on time/space complexity, edge cases, and how I explain my thought process.”
Gemini: Research Heavy, Multi‑Modal Companion
Gemini is good at:
- Reading and summarizing long documents (example: system design blogs, company engineering blogs).
- Explaining language‑specific details in C++ and Java.
- Cross‑checking answers you get from other tools.
How to use Gemini:
- Paste a system design article (example on designing a URL shortener) and ask:
“Summarize the key design decisions in this article in bullet points. Then give me 3 follow‑up questions an interviewer might ask about this design.”
2. After solving a DSA problem, paste your solution and ask:
“Critique this C++ solution for a FAANG interview. Is there a cleaner or more idiomatic approach?”
NotebookLM: Your Second Brain for Interview Prep Notes
NotebookLM shines when you have a lot of material: notes, PDFs, blog posts and want a single place to query them.
Typical use cases for interview prep:
Create a notebook for “System Design Patterns” and upload:
- Your notes on caching, sharding and load balancing
- PDFs/articles on “Design Twitter”, “Design Netflix” & “Design Uber”
Create a notebook for “DSA Patterns” with:
- Your notes on DP patterns, sliding window, binary search on answer, etc.
Create a notebook for “Company‑specific prep” (example: Amazon, Google) with:
- Glassdoor‑style and Leetcode experiences
- Blog posts from the company’s engineering blog
Then ask questions like:
“In this notebook, what are the common patterns used for designing a URL shortener and a news feed? Summarize similarities and differences in less than 10 bullet points.”
“Generate 10 flashcards from my system design notes with a question on the front and a short answer on the back.”
NotebookLM turns your scattered prep resources into a searchable interview playbook.
Using AI as a Mock Coding Interviewer
Let’s walk through how to set up realistic mock coding interviews.
Step 1: Set the context and difficulty
Tell the AI exactly what you want:
“Act as a technical interviewer for a software engineer role at a FAANG style company. I’ll answer in C++. Ask me one medium and one hard LeetCode‑style problem focused on arrays and graphs. Don’t show solutions unless I ask. Ask clarifying questions if my approach is unclear and at the end give detailed feedback on my performance.”
A typical first exchange might look like:
AI: Great. Let’s start with a medium question.
Question 1:
Given an array of integers, return the length of the longest subarray with sum equal to k.
You can assume the array may contain negative numbers. Please restate the problem in your own words and describe your initial thoughts before coding.
You then respond with your thought process and gradually move to code.
Step 2: Actually code the solution (outside the chat)
Do not just “think through” the solution. Open your IDE or LeetCode and write actual C++/Java code. Then paste back your code in the chat for review. Then ask:
“Evaluate this solution as an interviewer. What’s the time and space complexity? What follow‑up optimizations or edge cases would you ask about?”
Step 3: Push for follow‑ups
Good interviewers often add follow‑ups:
- “What if the array is streaming and very large?”
- “Can you handle this in O(1) extra space?”
- “Can you support updates between queries?”
Prompt the AI:
“Give me 2–3 realistic follow‑up questions an interviewer might ask for this problem and evaluate my answers.”
Example Java prompt for beginners (students):
“I’m a final‑year CS student in India preparing for product‑based companies. Act as an interviewer for a 45-minute online coding round. Ask me one easy and one medium problem in Java on arrays and strings. Don’t help unless I ask for a hint. At the end, rate my performance out of 10 and give me 3 specific areas to improve.”
Using AI as a Mock System Design Interviewer
From around 2-3 years of experience onward, system design starts to matter a lot especially for mid‑level engineers targeting senior roles.
Mock Interview: “Design a URL Shortener”
Try a prompt like:
“Act as a senior backend engineer at a FAANG‑style company. Run a 60‑minute system design interview with me. Start with the question: ‘Design a URL shortener like bit.ly’.
Rules:
1. Ask me clarifying questions and let me answer.
2. Let me propose an initial high‑level design first.
3. Then push back on scalability, database schema, sharding, caching and rate limiting.
4.At the end, give me feedback in these categories: requirements, high‑level design, data model, scaling, trade‑offs and communication.”
You then answer with assumptions (example: 100M URLs, 10K QPS, reads >> writes), and the AI will adapt.
Mock Interview: “Design Twitter / News Feed”
For a more advanced system design:
“Run a system design interview for a mid‑level engineer targeting senior roles. Ask me to ‘Design Twitter’. After I give a high‑level design, dig deep into feed generation, fan‑out vs fan‑in, database partitioning and caching. Ask me to estimate rough capacity (users, tweets per second, storage). Give critical feedback like an experienced interviewer at Meta or Google.”
Ask for company‑style feedback:
“After my design, tell me if this would be considered ‘hire’, ‘no hire’, or ‘weak hire’ for a senior level at a Big Tech company. Be strict.”
Sample Prompts You Can Copy-Paste
Here are ready‑to‑use prompts for different career stages.
For University Students (0 years experience)
Coding focus, C++/Java:
“I’m a final‑year CS student preparing for FAANG‑style interviews. Act as a mentor and interviewer. Create a 2‑week plan focusing on data structures and algorithms in C++ (or Java), with daily topics and 2–3 practice problems per day. Assume I know the basics but need to reach a competitive level for product‑based companies in India and the US.”
Concept explanations:
“Explain dynamic programming to me with 3 examples (Fibonacci, knapsack, longest increasing subsequence). Please include how I should talk through these in an interview to show strong communication.”
For Early‑Career Engineers (0–3 years)
Mixed coding + basic system design:
“I have 1.5 years of backend experience in Java. Help me prepare for interviews at product‑based companies in India and the US. Create a 3‑week plan that mixes:
1. 60% coding (medium LeetCode)
2. 25% basic system design (URL shortener, rate limiter, key‑value store)
3. 15% behavioral questions.
Include specific topics and sample questions for each day.”
For Mid‑Level Engineers (3–5 years) Targeting Senior Roles
System design heavy:
“I have 4 years of experience as a backend engineer (Java, microservices). Act as an interviewer for a senior role at a Big Tech company. Run a 60‑minute system design interview on one of:
1. Design Twitter
2. Design an e‑commerce checkout system
3. Design a distributed cache.
You choose one and run the interview end‑to‑end, including probing on trade‑offs and failure scenarios.”
Feedback on leadership/impact:
“Here is how I’d answer the behavioral question ‘Tell me about a time you led a project end‑to‑end’. Critique it using the STAR method and tell me if it fits expectations for a senior engineer in Big Tech and large product companies.”
Limitations: What AI Cannot Replace
It’s important to be realistic about what AI cannot do.
1. Real interviewer judgment & company‑specific expectations
- AI doesn’t know the exact hiring bar at a specific company team or location
- It can approximate “Big Tech standards,” but it’s not a perfect match for Culture fit or Team needs
2. True interview pressure and communication practice
- Chatting with AI is lower pressure than a real interviewer
- You still need to practice thinking and speaking under pressure, handling interruptions and responding to unexpected questions
3. AI can hallucinate
- AI can give incorrect code or use non-existent APIs or libraries
- Run code on a judge and cross-check complex explanations accross more than one source
4. Deep domain expertise
For niche domians (ex: very specific distributed databases), AI may give generic answers. For senior roles, supplement AI with:
- Official docs
- High‑quality books
- Company engineering blogs and talks
Conclusion: Use AI as a Force Multiplier, Not a Crutch
AI tools like ChatGPT, Gemini, and NotebookLM can turn your interview prep from random grinding into structured, feedback‑driven practice:
- ChatGPT and Gemini give you on‑demand mock interviews, explanations, and targeted drills.
- NotebookLM turns your scattered notes into a queryable second brain for system design and DSA patterns.
- Realistic prompts and strict rules help you mirror your dream company interview.
The true power of AI is not code generation; it is its capacity to be an infinitely scalable, brutally objective, and context-aware interview co-pilot. Use it to accelerate your learning, master the human skills of trade-off and communication and walk into your next interview with confidence.