If You Can Explain THIS, You’ll Crack Any Gen AI Interview 😳 (RAG System Design)RAG,LLM Interview

Description

what is retrieval augmented generation and why is it one of the most asked topics in generative ai interviews right now? in this tech by sketch episode, we explain rag (retrieval augmented generation) completely from scratch using simple visual storytelling and system design thinking — without complex code. you will learn how modern ai systems use: • chunking • embeddings • vector databases • similarity search • retrieval • llm generation we also explain: • how vector databases work • what cosine similarity means • how semantic search works • why rag reduces hallucination • difference between rag and fine tuning • how real world ai chatbots actually work this video is designed for: • engineering students • software engineers • ai enthusiasts • data science learners • professionals preparing for gen ai interviews • anyone learning llm system design if you can explain rag clearly, you are already ahead of most candidates in ai interviews. 🎯 free rag project pdf: subscribe and type “ai” in comments if you want the complete end to end rag project pdf. topics covered: rag explained retrieval augmented generation vector database explained embeddings explained semantic search cosine similarity llm architecture gen ai interview preparation rag system design pinecone faiss weaviate chroma db llm applications ai chatbot architecture #genai #rag #llm #artificialintelligence #machinelearning #datascience #openai #chatgpt #vectordatabase #embeddings #ragexplained #softwareengineering #systemdesign #aiinterview #techbysketch