The 250+ real questions they asked instead, and the guide I built to answer them.
Two months. More than 40 GenAI interviews across service companies, product companies and startups.
I expected the usual: define RAG, explain embeddings, compare vector databases. That's not what happened.
The first question was almost always easy. Then came the follow-ups:
- "How would you measure that?"
- "What breaks at 10x traffic?"
- "Your fix just doubled the cost. Now what?"
That's where most candidates, me included in the early rounds, lose the offer. Interviewers aren't testing whether you've read the docs. They're testing whether you've shipped AI to production and fixed it when it broke.
After 11+ years in IT and 4+ years building production LLM systems, I noticed the same patterns again and again:
- Trade-offs beat tool names. "LangChain + Pinecone" is not an answer. Why you chose it, what it costs and what you gave up is.
- Every answer needs a metric. Recall@k, faithfulness, p95 latency, cost per conversation. No metric, and the interviewer assumes you haven't shipped it.
- Agents and security are now standard. Loop detection, tool permissions, prompt injection, human approval for risky actions.
- Cost comes up in almost every round. Model routing, caching, prompt reduction, smaller models.
So I wrote down every question I faced, and built full answer guides for each one.
What's inside the 159-page guide
250+ Real-Time Generative AI Interview Questions with Answer Guides has three parts:
| Part | What you get |
|---|---|
| 150 production scenario questions | Full answer guide for each: core answer, step-by-step approach, trade-offs, metrics, an example and how to handle follow-ups |
| 50 core theory questions | Concise answers with the key formulas interviewers expect |
| 100 interviewer follow-ups | The probes used to dig deeper, and what a strong answer covers |
The questions span 18 categories, including RAG pipeline design, embeddings and vector databases, hallucinations and citations, AI agents and agent safety, multi-agent systems, model selection and routing, cost optimization, LLMOps and CI/CD, evaluation and monitoring, security, compliance and governance, multi-tenant platforms, and system design.
It's built for AI Engineer, GenAI Engineer, LLM Engineer and AI Architect roles.
Sample questions from the guide
Here are a few real-style questions from the book. Try answering each one out loud before reading on.
1. "Retrieval returns the right documents, but the LLM still answers wrong. Why?"
Weak answer: "The model is hallucinating, so I'd switch to a bigger model."
What a strong answer covers: the right chunk may be buried mid-context (lost in the middle), chunks may be conflicting or stale, or the prompt may not force the model to answer only from context. You'd check faithfulness scores, reorder by relevance, add a reranker, and require citations per claim.
The full guide walks through the debugging steps, the metrics to track and the follow-ups interviewers ask next.
2. "Your agent keeps calling the same tool in a loop. How do you detect and stop it?"
What a strong answer covers: step and token budgets, detecting repeated tool calls with the same arguments, a max-retry policy per tool, and escalating to a human or a fallback path instead of failing silently.
The guide adds the trade-offs (when strict limits break legitimate long tasks) and how to log it for observability.
3. "Your CTO wants LLM costs cut by 70% without users noticing. What's your plan?"
What a strong answer covers: measure cost per conversation first, then model routing (small model first, large model on fallback), semantic caching, prompt compression, and an eval set to prove quality didn't drop.
More questions you'll find inside (answers in the guide)
- Your chatbot started hallucinating after yesterday's deployment. No code changed. Walk me through your debugging.
- A new model beats yours on benchmarks. Do you swap it into production today?
- Your RAG system returned confidential documents to the wrong employee. What do you do in the first hour?
- What happens if you remove positional encoding from a Transformer?
If you couldn't answer all of these with a metric and a trade-off, this guide is for you.
Who this guide is for
- Software engineers and data engineers switching into GenAI roles
- ML engineers moving from classic ML to LLM systems
- Experienced engineers preparing for senior GenAI, LLM Engineer or AI Architect interviews
- Anyone who has built a RAG demo but struggles to talk about production
Get it for just ₹51
One interview round can change your salary by lakhs. This guide costs less than a cup of coffee.
Get the 250+ Questions Guide →Full 159-page PDF, delivered instantly after purchase.
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Ritesh Sinha, AI Architect · @sql_interview_question
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