RAG System Design Interview Complete Guide (Embeddings, Chunking, Vector DB) | AI Engineer Interview

The AI How · Advanced ·🔍 RAG & Vector Search ·2mo ago

About this lesson

Your interviewer says "Design a RAG system for 50,000 internal documents." This is the #1 AI system design interview question in 2026, and most candidates get it completely wrong. In this video, I break down the complete RAG pipeline, every design decision, and the exact framework to nail this interview question. Timestamps: 00:00 - The Interview Question 00:26 - What Is RAG (Retrieval Augmented Generation) 01:24 - The Complete RAG Pipeline 02:33 - Embeddings Deep Dive 03:37 - Chunking Strategies 05:03 - Vector Database Selection 06:15 - RAG vs Fine-Tuning vs Long Context 07:42 - Three Generations of RAG 09:10 - The Interview Framework 10:23 - What's Next What you'll learn: - The 6-step RAG pipeline every interviewer expects - Embedding models comparison (Cohere, OpenAI, Voyage AI) - Chunking strategies ranked by sophistication - Vector DB selection criteria (Pinecone vs Weaviate vs Qdrant vs pgvector) - When NOT to use RAG (fine-tuning vs long context) - The 3 generations of RAG (Naive, Advanced, Agentic) - A 5-step interview answer framework This video tackles the common "Design a RAG System" interview question, a crucial topic in AI architecture for 2026. We explain how "retrieval augmented generation" helps "large language models" access data without retraining, ensuring accuracy and cost-effectiveness. Learn about document chunking strategies and how a "vector database" stores information, providing essential "design principles" for your system. Based on "The AI Engineer's System Design Interview Guide" by Lamhot Siagian. This is Video 1 of a 10-part AI System Design Interview series. Subscribe to catch them all.

Original Description

Your interviewer says "Design a RAG system for 50,000 internal documents." This is the #1 AI system design interview question in 2026, and most candidates get it completely wrong. In this video, I break down the complete RAG pipeline, every design decision, and the exact framework to nail this interview question. Timestamps: 00:00 - The Interview Question 00:26 - What Is RAG (Retrieval Augmented Generation) 01:24 - The Complete RAG Pipeline 02:33 - Embeddings Deep Dive 03:37 - Chunking Strategies 05:03 - Vector Database Selection 06:15 - RAG vs Fine-Tuning vs Long Context 07:42 - Three Generations of RAG 09:10 - The Interview Framework 10:23 - What's Next What you'll learn: - The 6-step RAG pipeline every interviewer expects - Embedding models comparison (Cohere, OpenAI, Voyage AI) - Chunking strategies ranked by sophistication - Vector DB selection criteria (Pinecone vs Weaviate vs Qdrant vs pgvector) - When NOT to use RAG (fine-tuning vs long context) - The 3 generations of RAG (Naive, Advanced, Agentic) - A 5-step interview answer framework This video tackles the common "Design a RAG System" interview question, a crucial topic in AI architecture for 2026. We explain how "retrieval augmented generation" helps "large language models" access data without retraining, ensuring accuracy and cost-effectiveness. Learn about document chunking strategies and how a "vector database" stores information, providing essential "design principles" for your system. Based on "The AI Engineer's System Design Interview Guide" by Lamhot Siagian. This is Video 1 of a 10-part AI System Design Interview series. Subscribe to catch them all.
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Chapters (10)

The Interview Question
0:26 What Is RAG (Retrieval Augmented Generation)
1:24 The Complete RAG Pipeline
2:33 Embeddings Deep Dive
3:37 Chunking Strategies
5:03 Vector Database Selection
6:15 RAG vs Fine-Tuning vs Long Context
7:42 Three Generations of RAG
9:10 The Interview Framework
10:23 What's Next
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