The RAG Questions Interviewers Are Really Asking Now

Recently, I faced an interview for a #Generative #AI 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. 🚀
🔥 #Important #RAG 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?
#GenerativeAI #GenAI #RAG #LLM #ArtificialIntelligence #MachineLearning #AIEngineer #LLMEngineer #GenerativeAIEngineer #InterviewPreparation #TechInterview

During a recent interview for a Generative AI Engineer role, the discussion centered heavily on RAG, LLMs, and production-grade AI systems, going beyond basic definitions into how such systems are built, evaluated, optimized, secured, monitored, and scaled. Topics covered included RAG architecture and its comparison to fine-tuning, chunking strategy, embeddings, vector similarity search, Top-K selection, hybrid search, hybrid RAG, reranking, and query rewriting. Further areas included improving retrieval quality, reducing hallucinations, handling out-of-knowledge-base queries, evaluating retriever and generation performance separately, managing large context windows, reducing latency, and scaling systems from thousands to millions of queries or documents. Additional topics included caching strategies, production monitoring, security risks such as prompt injection, implementing guardrails, and designing multi-tenant RAG systems.