Gen AI Interview Questions from 32 Job Offers (LLMs, RAG, Fine-Tuning, LangChain) | 2026

I cracked 32 Gen AI job offers — here are the EXACT interview questions they asked me. If you're preparing for a Generative AI, LLM Engineer, RAG Engineer, or AI/ML role in 2026, this is the most real, experience-backed interview prep video you'll find. No fluff. No theory padding. Just real questions from real interviews — covering everything from embeddings to MCP to agentic pipelines. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🔗 Download the PDF here - https://topmate.io/thisrohits/2067441 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🔗 Connect with me ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🔹 My LinkedIn →   / thisrohit   🔹 Topmate→ https://topmate.io/thisrohits/ 🔹 Instagram →   / thisrohits   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📌 TOPICS COVERED ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ✅ LLM Interview Questions — GPT, Claude, Gemini, Mistral, LLaMA ✅ RAG (Retrieval-Augmented Generation) — chunking, indexing, reranking ✅ Embeddings — text embeddings, dense vs sparse, embedding models ✅ Vector Databases — FAISS, ChromaDB, Pinecone, Weaviate, Qdrant, Milvus ✅ MCP (Model Context Protocol) — architecture & real interview Qs ✅ Fine-tuning — LoRA, QLoRA, SFT, RLHF, PEFT ✅ Prompt Engineering — chain-of-thought, few-shot, system prompts ✅ LangChain & LangGraph — chains, agents, memory, tools ✅ OpenAI API — function calling, assistants API, structured outputs ✅ Agentic AI & Multi-Agent Frameworks — ReAct, AutoGen, CrewAI ✅ GenAI System Design — end-to-end RAG pipelines, scalability ✅ Hugging Face — model hub, transformers, inference endpoints ✅ Semantic Search — cosine similarity, ANN, HNSW indexing ✅ Knowledge Graphs + RAG — GraphRAG, Neo4j integration ✅ Guardrails, hallucination reduction & evaluation (RAGAS, DeepEval) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 👤 WHO AM I? ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 5+ years specializing in Generative AI | 32 job offers in Gen AI roles I work with LLMs, RAG pipelines, fine-tuning, LangChain, vector databases, embeddings, MCP servers & cloud AI infrastructure daily. This channel is where I share what actually works — not textbook theory. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ⏱️ TIMESTAMPS ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 00:00 Intro & my Gen AI journey (32 offers story) 01:02 What is Gen AI? Transformer architecture explained 02:00 What is RAG? Retrieval Augmented Generation 02:35 RAG pipeline step-by-step breakdown 04:27 Embeddings & vector databases (FAISS, Chroma, Pinecone) 06:09 Chunking strategies for RAG 06:41 Fine-tuning vs RAG — when to use what 07:51 LoRA, QLoRA & PEFT explained 08:08 LangChain components & AI agents 09:00 Performance, latency & scalability in Gen AI apps 09:34 Caching strategies to reduce token cost 10:06 Guardrails — what they are & how to implement 10:31 Key revision checklist (self-attention, positional encoding, BERT vs GPT) 11:49 Prompt injection & dynamic guardrails 11:58 MCP (Model Context Protocol) — interview goldmine 12:37 Production mindset — what top candidates do differently 13:34 Final tip: how to impress any Gen AI interviewer ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🔍 KEYWORDS THIS VIDEO COVERS ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ generative ai | gen ai | large language models | LLM | RAG | retrieval augmented generation | embeddings | vector database | FAISS | ChromaDB | Chroma | Pinecone | Weaviate | Qdrant | Milvus | OpenAI | GPT-4 | Claude | Gemini | Mistral | LLaMA | LangChain | LangGraph | MCP | Model Context Protocol | fine-tuning | LoRA | QLoRA | RLHF | PEFT | prompt engineering | semantic search | HNSW | cosine similarity | transformer | attention mechanism | Hugging Face | agentic AI | AutoGen | CrewAI | ReAct | multi-agent | knowledge graph | GraphRAG | hallucination | RAGAS | DeepEval | AI interview | gen ai interview questions 2026 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ #GenAI #GenerativeAI #LLM #RAG #RetrievalAugmentedGeneration #LangChain #LangGraph #Embeddings #VectorDatabase #FAISS #ChromaDB #Pinecone #Weaviate #Qdrant #Milvus #OpenAI #GPT4 #Claude #Gemini #Mistral #LLaMA #HuggingFace #FineTuning #LoRA #QLoRA #RLHF #PEFT #PromptEngineering #MCP #ModelContextProtocol #AgenticAI #AutoGen #CrewAI #MultiAgent #SemanticSearch #HNSW #Transformer #GraphRAG #AIInterview #GenAIInterview #LLMEngineer #RAGEngineer #AIJobs #MLEngineer #AICareer #TechInterview #GenAI2026 #AIInterviewPrep #DataScience #MachineLearning #DeepLearning #PythonAI #AIMLindia