# Why ChatGPT Fails as an Interview Tutor (And How I Built a Better One with Claude Code)

> Source: <https://dev.to/guanyi_liu_21a5d7417eb332/why-chatgpt-fails-as-an-interview-tutor-and-how-i-built-a-better-one-with-claude-code-p94>
> Published: 2026-05-30 13:16:19+00:00

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_`

and`not_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](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.