ChatGPT Always Says "Great!", But I Didn't Remember Anything — So I Built a Real Interview Coach with Claude Code

A Pain Point for Everyone

When you use ChatGPT / Claude to prepare for interviews, have you ever experienced this:

• You give an obviously problematic answer, and it replies "Great answer!" then moves directly to the next question.
• Every time you start a new conversation, it doesn't remember what you learned yesterday, so you have to introduce yourself from scratch.
• You memorized a bunch of boilerplate answers, but don't know when to review them, and forget everything within a couple of days.
• It explains a concept to you, and you think you understand it after reading, but when it's time for the interview, you can't articulate it.

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

Its operating principle is "to satisfy the user," not "to help the user learn." It thinks everything you say is good because it's afraid you'll be unhappy.

Is There a Way to Make AI a Strict Teacher?

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. Simply put, you can "program" the AI's behavior by writing rules files, and these rules automatically load 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. Mix questions (from 3-4 modules, not just one direction)
2. Correct answer → Brief confirmation + related knowledge points → Next question
3. Wrong answer → Must explain (including reference URL) → Give a verification question → Only new question after passing
4. No skipping steps; cannot give the next question immediately after an answer

The difference from regular AI: The rules are hardcoded, not decided by the AI itself. It can't get lazy and say "Great" because the rules file dictates it must follow the explanation→verification process.

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

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 track when to review yourself.

3. Daily Wrap-up Verification

Say "That's it for today" at the end of the day to trigger the wrap-up process:

1. Claude provides an outline of today's study.
2. You recall based on the outline and give your own understanding.
3. Claude judges whether you truly understand or are just reciting answers.
4. Updates the review schedule.

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

Effect Comparison

Core difference: Other tools either have memory but no understanding (Anki), understanding but no memory (ChatGPT), or neither (SaaS). Claude Code's rules file + memory system is the only solution that combines both.

The Biggest Killer Feature: Starting from Your Project Code

All interview tools on the market work as "I ask, you answer."

This project is different: It asks follow-up questions based on your own code.

For example, my project has a BoundedQueue (bounded queue) that uses two condition_variable. During interview prep, Claude doesn't just ask you to "explain condition_variable"; it asks:

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

This type of question forces you to think about the principles from code you've actually written, giving knowledge an anchor point instead of just memorizing boilerplate.

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 Your First Command

I want to prepare for a C++ backend development interview, targeting an internship at a top company. Start reviewing.

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

4. Switch to Your Own Tech Stack

The repository comes with sample question banks for C++ backend. If you use Java / Go / frontend, replace them with the blank template:

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

My User Experience

I am a sophomore student preparing for the 2026 autumn recruitment internship. After using this system to prepare for C++ backend interviews, the most obvious feelings are:

1. No more anxiety — No need to self-plan "what to review today"; the system automatically schedules based on the forgetting curve.
2. Actually remembered — The wrong answer → explanation → verification process forces true understanding, not just a passing glance.
3. Confident about my projects — Follow-up questions starting from your own code let you clearly explain the "why" behind every design decision in your project.

This Isn't Just an Interview Tool

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

Interviewing is just one scenario. You can use it to prepare for:

• Technical interviews (currently supported)
• Career change/job-hopping review
• Final exams (same principle, just swap the knowledge bank)

Open Source Link

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

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