When you use ChatGPT / Claude to prepare for interviews, have you ever encountered this situation:
The root cause is simple: ChatGPT is an attentive chat companion, not a strict teacher.
Its working principle is "make the user satisfied," not "make the user learn." It thinks everything you say is great because it's afraid you'll be unhappy.
Yes. I did it using Claude Code (Anthropic's CLI coding tool).
Claude Code has a unique capability: Rule files (CLAUDE.md) + Persistent memory system. In simple terms, you can "program" the AI's behavior by writing rule files, and these rules are automatically loaded in every conversation.
I applied this capability to interview preparation.
I wrote a set of configuration files, with two core components:
Rules:
1. Mixed question sets (3-4 modules, not just one direction)
2. Correct answer → Brief confirmation + Related knowledge points → Next question
3. Wrong answer → Must explain (including reference URLs) → Verification question → New question only after passing
4. No skipping steps. Cannot ask the next question immediately after an answer.
The difference from a regular AI: The rules are hard-coded, not decided by the AI itself. It can't be lazy and say "Great," because the rule file tells it it must complete the explanation → verification process.
| Knowledge Point | First Learned | D+1 | D+2 | D+4 | D+7 | D+15 | Status |
|---|---|---|---|---|---|---|---|
| Smart Pointers | 5/29 | ✅ | ✅ | Mastered | |||
| Virtual Function 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.
Say "That's it for today" to trigger the wrap-up process:
This is the AI version of the Feynman Technique — not just feeling like you understand, but being able to explain it, with AI as the judge.
| 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 | No (Only gives answer) | None | Partial | Yes |
| Native Question Generation from Project Code | Need to manually paste code | N/A | Requires file upload | Not supported | Native Support |
| Additional Cost | None | None | None | $29-300/time | No Extra Cost |
The core difference: Other tools either have memory without understanding (Anki), or understanding without memory (ChatGPT), or neither (SaaS). Claude Code's rule file + memory system is the only solution that combines both.
All interview tools on the market are "I ask, you answer."
This project is different: Asking follow-up questions starting from your own code.
For example, my project has a BoundedQueue
(Bounded Queue) using two condition_variable
. During interview prep, Claude doesn't ask you "Please explain condition_variable," but rather:
"Your BoundedQueue uses two condition_variables (
not_full_
andnot_empty_
). Can you use just one?"
This kind of question forces you to think about the principles from code you've written yourself, giving the knowledge an anchor point, not just memorizing standard answers.
gh repo fork happiness-cheng/ai-interview-engine --clone
cd ai-interview-engine
claude
I want to prepare for a C++ backend developer interview, aiming for a big tech internship. Let's start reviewing.
That's it. Claude will read the rule files and start the first round of questions.
The repository comes with an example question bank for C++ backend. If you use Java / Go / Frontend, replace it with the blank template:
knowledge/TEMPLATE.md → Copy to interview_tracker.md → Fill in your knowledge points
I'm a sophomore student preparing for the 2026 fall internship recruitment. Using this system for C++ backend interview prep, the most noticeable feelings are:
The underlying engine is general-purpose: Question → Answer → Judge → Explain → Verify → Forgetting Curve Scheduling.
Interviews are just one scenario. You can use it to prepare for:
GitHub: https://github.com/happiness-cheng/ai-interview-engine
If it helps you, give it a star. If you have suggestions for improvement, feel free to open an issue.