RAG Interview Prep: From Chunking to Multi-Tenant Scaling
I thought of sharing some of the important RAG questions that were discussed during the interview. Hopefully, these will be helpful for anyone preparing for GenAI / AI Engineer / LLM Engineer roles. 🚀
🔥 hashtag hashtag Interview Questions
🔹 What is RAG and why do we need it?
🔹 RAG vs Fine-tuning — when would you choose which?
🔹 Explain the complete RAG architecture.
🔹 How do you decide chunk size and chunk overlap?
🔹 What are embeddings and how do they work?
🔹 How does vector similarity search work?
🔹 How do you choose Top-K?
🔹 What is Hybrid Search?
🔹 What is Hybrid RAG and how is it different from Hybrid Search?
🔹 Why do we need reranking?
🔹 What is Query Rewriting and why is it useful?
🔹 How do you improve poor retrieval quality?
🔹 How do you reduce hallucinations in RAG?
🔹 How do you handle questions when relevant information is not available in the knowledge base?
🔹 How do you evaluate Retriever performance?
🔹 How do you evaluate the final LLM response?
🔹 How do you identify whether an issue is with Retrieval or Generation?
🔹 How would you handle a large context window?
🔹 How do you reduce latency in a production RAG system?
🔹 How would you scale RAG from 100 queries to 100K+ queries?
🔹 How would you scale a vector database from 10K to 1M+ documents?
🔹 Where would you use caching in RAG?
🔹 How do you monitor a RAG system in production?
🔹 What are common security risks in RAG?
🔹 What is Prompt Injection and how do you prevent it?
🔹 How do you implement Guardrails in a RAG application?
🔹 How would you design a multi-tenant RAG system?
💡 Key takeaway
Modern GenAI interviews are going beyond just asking “What is RAG?”
Interviewers are increasingly focusing on how you would build, evaluate, optimize, secure, monitor, and scale RAG systems in production.
If you're preparing for a GenAI / LLM / RAG Engineer interview, I hope this list helps you identify the areas you should focus on.
I’ll be covering these questions with practical explanations and real-world architecture examples in upcoming posts.
What RAG topic do you find the most challenging?
This post isn't really fact-checkable in the usual sense — it's a personal anecdote ("I had an interview") plus a list of interview questions, not verifiable external claims. There's no company announcement, statistic, or specific event to search and confirm. A few observations instead:
Nothing to verify: The interview experience itself is a first-person claim I can't check, and the question list is just... a list of questions. There's no factual assertion like "X launched" or "Y raised $Z" to test against sources.
Terminology check — all legitimate: The concepts named (RAG, hybrid search, hybrid RAG, reranking, query rewriting, chunk size/overlap, Top-K, prompt injection, guardrails, multi-tenant RAG) are real, standard terms in the GenAI engineering space, used correctly and in a sensible progression (fundamentals → architecture → optimization → production/scale → security). Nothing here is fabricated or misused.
One soft flag: "Hybrid RAG" vs "Hybrid Search" is a real distinction some practitioners draw, but the terminology isn't fully standardized industry-wide — different teams/blogs define "Hybrid RAG" slightly differently (e.g., combining RAG with fine-tuning, vs. combining retrieval methods with agentic steps). If you cover this in your follow-up post, worth stating your own definition explicitly rather than implying it's a universally agreed-upon term.
Bottom line: No factual errors to correct — it's a clean, accurate list. If you want, I can help fact-check specific answers once you draft them (e.g., claims about latency numbers, specific tool comparisons, or scaling benchmarks), since that's where posts like this usually introduce oversimplified or outdated claims.