GenAI Engineer, AI Engineer, and LLM Engineer

Published 2026-08-21 in AI Agents

Recently, I faced an interview for a Engineer role, and the interview was heavily focused on RAG, LLMs, and production-grade AI systems. 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. 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…

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