Top 65 Scenario Questions

Q1: How do you reduce hallucinations in RAG systems?

Q2: Design prompts for reliable JSON output

Q3: Design a multi-agent research system

Q4: Design a safe Claude Code workflow for a monorepo

Q5: Design Cursor rules and review gates for a product team

Q6: Codex CLI cost optimization for high-volume agent usage

Q7: System design: AI coding agent platform for 1,000 engineers

Q8: MCP security interview: threat model for agent tools

Q9: Prompt injection interview questions for coding agents

Q10: Repository context engineering at company scale

Q11: Secure AGENTS.md and repository config attacks

Q12: AI governance interview: approval workflows for agents

Q13: Evaluating coding agent quality in CI

Q14: Why Do Large Language Models (LLMs) Hallucinate?

Q15: Types of LLM Hallucinations & Why RAG Isn't Enough