Stop Saying 'Great!'—Build a Real AI Interview Coach with Claude Code

ChatGPT always says "Great!", but I remember nothing — I built a real interview tutor with Claude Code

A universal pain point

When using ChatGPT / Claude to prepare for interviews, have you ever encountered these situations:

• You gave a clearly flawed answer, it replied "Great answer!" and moved directly to the next question
• Every time you start a new chat, it doesn't remember what you studied yesterday, you have to introduce yourself from scratch
• You memorized a bunch of standard answers, but don't know when to review, so you forget everything after a couple of days
• It explains a concept, you think you understand after reading, but when it's interview time, you can't articulate it

The root cause is simple: ChatGPT is an accommodating companion, not a strict teacher.

Its operational principle is "keep the user satisfied," not "help the user learn." It agrees with everything you say because it fears upsetting you.

Is there a way to make AI a strict tutor?

Yes. I achieved this using Claude Code (Anthropic's CLI programming tool).

Claude Code has a unique capability: Rules files (CLAUDE.md) + persistent memory system. In simple terms, you can "program" the AI's behavior by writing rules files, and these rules are automatically loaded in every conversation.

I applied this capability to interview preparation.

What I Did

I wrote a set of configuration files, with two core components:

1. Strict Learning Process (rules/interview.md)

Rules:
1. Mixed questioning (3-4 modules, not just one topic)
2. Correct answer → Brief confirmation + Related knowledge points → Next question
3. Wrong answer → Must explain (including reference URL) → Verification question → Proceed only after passing
4. No skipping steps, cannot jump directly to the next question after answering

Compared to standard AI: Rules are fixed, not AI's own decision. It cannot be lazy and say "Great" because the rules file dictates it must complete the explanation → verification process.

2. Forgetting Curve Review Scheduling (knowledge/interview_tracker.md)

Knowledge Point	First Learned	D+1	D+2	D+4	D+7	D+15	Status
Smart Pointers	5/29	✅	✅				Mastered
Virtual Table	5/29	❌					Weak

At the start of each session, Claude automatically checks which knowledge points are due for review and prioritizes review questions. You don't need to remember when to review yourself.

3. Daily Closing Verification

Saying "That's it for today" triggers the closing process:

1. Claude provides a summary of today's study outline
2. Based on the outline, you recall and give your own understanding
3. Claude judges whether you truly understand or are just reciting answers
4. Update the review schedule

This is the AI version of the Feynman learning technique — it's not about you thinking you understand, but about you being able to explain it, with AI as the judge.

Feature Comparison

Capability	ChatGPT Interview	Anki	NotebookLM	SaaS Platforms	This Project
Cross-session memory	None	Deck-level only	None	Partial	Yes
Spaced repetition	None	Yes	None	None	Yes
Wrong → Explain → Verify	None	None (Answers only)	None	Partial	Yes
Project-based native questions	Manual code pasting	N/A	File upload required	Not supported	Native support
Additional cost	None	None	None	\$29-300/session	No extra cost

Core difference: Other tools either have memory without understanding (Anki), or have understanding without memory (ChatGPT), or lack both (SaaS). Claude Code's rules file + memory system is the only solution that combines the two.

The Biggest Killer Feature: Starting from Your Project Code

All interview tools on the market follow a "I ask, you answer" model.

What this project does differently: Starts questioning from your own code.

For example, my project has a BoundedQueue (bounded queue), using two condition_variable. During interview preparation, Claude doesn't ask "Please explain condition_variable," but instead asks:

"Your BoundedQueue uses two condition_variables (not_full_ and not_empty_), would one be sufficient?"

Such questions force you to think about principles from code you've actually written, giving knowledge an anchor point instead of rote memorization of standard answers.

Quick Start

1. Fork the Repository

gh repo fork happiness-cheng/ai-interview-engine --clone

2. Open with Claude Code

cd ai-interview-engine
claude

3. Say the First Sentence

I want to prepare for C++ backend developer interviews, targeting internships at major companies. Let's start reviewing.

That's it. Claude will read the rules files and begin the first round of questions.

4. Switch to Your Own Tech Stack

The repository comes with sample question banks for C++ backend. If you're using Java / Go / Frontend, simply replace with the blank template:

knowledge/TEMPLATE.md → Copy to interview_tracker.md → Fill in your knowledge points

My User Experience

I'm a sophomore student preparing for the 2026 fall internship recruitment. Using this system for C++ backend interview preparation, the most noticeable effects are:

1. Less anxiety — No need to plan "what to review today," the system automatically schedules based on the forgetting curve
2. Actually remembering — The wrong → explain → verify process forces genuine understanding, not fleeting impressions
3. Confidence in projects — Questions starting from your own code make you able to clearly explain the "why" behind every design decision in your project

This is Not Just an Interview Tool

The underlying engine is universal: Question → Answer → Judge → Explain → Verify → Forgetting Curve Scheduling.

Interview preparation is just one scenario. You can use it for:

• Technical interviews (currently supported)
• Career change/job switch review
• Final exams (same principle, just change the knowledge base)

Open Source Address

GitHub: https://github.com/happiness-cheng/ai-interview-engine

If this helps you, give it a star. If you have suggestions for improvement, feel free to open an issue.