Generative AI Interview Questions in 10 Minutes | Freshers Revision

Preparing for a Generative AI interview tomorrow? Revise the 10 most asked topics in just 10 minutes, with simple definitions, real-time examples, workflows, and easy memory tricks. Made for freshers and candidates with around 1 year of experience. Pause at each question, try answering it yourself, then check the answer! TIMESTAMPS (adjust to your final video if needed) 0:00 Introduction 0:21 1. What is Generative AI? 1:20 2. LLM and Transformers 2:19 3. Tokens and Context Window 3:16 4. Prompt Engineering 4:07 5. Embeddings and Vector Databases 4:51 6. RAG (Retrieval-Augmented Generation) 5:40 7. Fine-tuning vs RAG vs Prompting 6:28 8. Hallucination and how to reduce it 7:15 9. AI Agents and Tool Calling 8:04 10. Evaluation, Safety and Guardrails 8:44 Quick recap of all memory tricks 9:14 Practice questions WHAT YOU WILL LEARN Difference between Generative AI and traditional AI How LLMs and Transformers work (attention, next-token prediction) Tokens, context window, and why they affect cost Zero-shot, few-shot, and chain-of-thought prompting Embeddings and vector databases (FAISS, Pinecone, Chroma) How RAG works step by step When to use prompting, RAG, or fine-tuning How to reduce hallucination AI agents, tool calling, and the think-act-observe loop Evaluation, guardrails, and prompt injection WHO IS THIS FOR? Freshers, final-year students, and developers with 0-2 years of experience preparing for Generative AI, LLM, or AI engineer interviews. If this video helped you, please LIKE, SHARE it with a friend who is preparing for interviews, and SUBSCRIBE (press the bell) for more interview-prep videos. Tell me in the comments which topic you want next! #GenerativeAI #AIInterviewQuestions #RAG TAGS (paste in the Tags field, comma separated) generative ai interview questions, gen ai interview questions and answers, generative ai interview preparation, llm interview questions, rag interview questions, prompt engineering interview questions, ai interview for freshers, gen ai for beginners, retrieval augmented generation, vector database explained, ai agents explained, hallucination in llm, fine tuning vs rag, generative ai in 10 minutes, ai interview questions 2026