2026 AI Engineering Interview 100 Questions Open-Sourced: Don't Memorize Answers—Start with Troubleshooting Logic

Someone open-sourced a '2026 AI Engineering Interview 100 Questions' on GitHub. It doesn't stop at merely listing questions. The author systematically reorganized the 100 questions and added an analysis of core assessment points and production-grade troubleshooting reference answers to each one, plus 7 major system design questions. The repo link is at the end.

Just looking at the questions easily makes you anxious, because you don't know what the interviewer is really trying to test. This question bank is reorganized into 8 sections: Prompt pitfalls, RAG retrieval, Agent state machine infinite loops, fine-tuning selection, vLLM inference queuing, production security and cost governance, etc. The questions look like this:

• A customer service Agent keeps calling the same tool 40 times with the same order number in an infinite loop, until Token timeout crashes it. How do you intercept this from the underlying runtime?
• The monitoring dashboard shows Faithfulness at 0.92, but the business team and real users are both complaining in the group chat that the system is unusable. Where is the problem?
• Monthly active traffic grew only 40%, but the month-end LLM API bill tripled. How do you report this to the CFO and come up with a cost-reduction plan?
• At 2 a.m. on Halloween, a multi-tenant system leaked another customer's confidential contract terms. How do you lead the emergency response and architecture refactor?

These questions don't feel like rote interview trivia. They map to real failures such as inaccurate RAG retrieval, runaway Agent loops, sudden cost blowups, and multi-tenant data leakage. The question bank also gives two senior AI architects' 'muscle memory': First, scope the problem first—was the correct evidence actually delivered in front of the model? If not, it's a retrieval and ingestion problem; no amount of Prompt tweaking will help. If it was delivered and the answer is still wrong, then it's a generation-stage problem. Second, commit to correct and measurable goals. Don't promise that a non-deterministic system will produce identical wording; promise stability in business decisions, using benchmark datasets and quantified SLOs as the team contract.

A netizen commented that adding troubleshooting approaches is extremely practical; simply memorizing questions still leaves you confused in production. That hits the nail on the head. Interview questions themselves don't make you smarter—they only make you more anxious. What makes this question bank worth reading is that it breaks down the troubleshooting path behind each question: which metrics to look at first, how to distinguish retrieval from generation, when to circuit-break, and when to hand off to a human. These approaches can be discussed in interviews and also used in production troubleshooting.

If you're preparing for interviews, don't memorize from question 1 to question 100. First locate the pit you're currently stepping in: those doing RAG should look at retrieval and evaluation; those doing Agents should look at tool calling and state machines; those being chased by bills should look at cost governance. The system design questions are especially worth reviewing: 7 questions cover end-to-end architectures such as enterprise RAG for 50,000 people, an e-commerce customer service Agent for 100,000 orders, a shared inference gateway, and emergency response for Halloween data leakage. Even without a technical background, you can see from these questions the real troubles of AI implementation: permissions, cost, latency, and failure handling.

The repo is open source; you can take it directly. The author has only one request: if it helps you, go to GitHub and give it a Star.

GitHub address: https://github.com/Kimberlying/ai-engineering-interview-100

Interview questions are always changing. People who can clearly explain production failures have always been few.