technical
Prepare for prompt-engineering interviews at frontier labs and applied-AI teams — prompt design patterns, evaluation methodology, hallucination mitigation, and the trade-offs behind real production LLM systems.
Sample questions
These are the kinds of questions you'll drill. Real mock rounds will generate many more — tailored to the exact JD you paste in.
Walk me through how you'd design an evaluation suite for a customer-support LLM.
Why does chain-of-thought work, and when does it fail?
Compare zero-shot, few-shot, and fine-tuning — which would you pick for a structured-extraction task and why?
How would you reduce hallucinations in a RAG pipeline? Walk through your toolkit.
Diagnose: a prompt works in dev but degrades in production traffic. Where do you look first?
Reading Prompt Engineering questions isn't the same as answering them live. You need the rep — the pause, the clarifying question, the moment where you realize you forgot the framework. We put you in that exact spot, then show you what a strong answer would have looked like.
Related topics
React
Hooks, rendering, and real-world patterns.
System Design
Scale, trade-offs, and clear reasoning.
SQL
Joins, indexes, and query plans.
Data Structures & Algorithms
Arrays, graphs, trees, and the classics.
Behavioural
STAR answers that actually land.
Leadership
Influence without authority, grown-up feedback.
Paste the JD, pick your topics, and go. Full session, full feedback, no fluff.
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