Applied AI / LLMs

Interview

How do you decide which tools to give an AI agent, and how many is too many?

Tool design is the main lever for agent reliability — bad tool sets cause wrong calls, loops, and security holes.

Interview

How would you design an AI agent that books meetings on behalf of a user?

Tests agent design fundamentals — tool design, safety, reliability under autonomy.

Interview

How would you build an evaluation suite for an LLM-powered feature?

Evals are the difference between shipping and praying. Standard interview topic in 2026.

Interview

An LLM hallucinated a fake API in production code. How do you prevent this systematically?

Tests whether you treat LLM reliability as an engineering problem, not a model problem.

Interview

What caching strategies do you use to cut LLM latency and cost in production?

Separates candidates who've run LLM apps at scale from those who only call APIs in notebooks.

Interview

How do you handle PII in a RAG pipeline without breaking retrieval quality?

Tests privacy-aware AI design — a gap between demo RAG and enterprise deployment.

Interview

How do you defend a production LLM application against prompt injection attacks?

Tests whether you treat LLM security as layered engineering, not a single prompt trick.

Interview

Walk me through designing a production RAG system for a docs chatbot.

System-design-style question that's now standard in AI engineering interviews.

Interview

When would you use RAG vs fine-tuning to give an LLM access to new information?

Tests practical LLM judgment — this is the #1 question in applied AI interviews in 2026.

Interview

Your LLM keeps returning invalid JSON for structured outputs. How do you fix it systematically?

Tests production reliability — structured outputs power agents, evals, and integrations; failures can't be hand-waved.