RAG Mock Interview Questions and Answers for GenAI Job Roles
In this video, we deconstruct the top 15 RAG interview questions and answers that separate novice developers from principal architects. We cover everything from the core foundations of retrieval augmented generation interview questions to advanced concepts like HNSW indexing, Agentic RAG, and Graph RAG.
Timestamps:
0:00 - Introduction: Why RAG is the Enterprise Standard
0:56 - Q1: What is RAG & Why is it critical for Enterprise AI?
1:48 - Q2: Core Components: Retriever vs. Generator
2:41 - Q3: Sparse vs. Dense vs. Hybrid Retrieval
3:30 - Q4: Indexing Strategies: HNSW vs. IVF Trade-offs
4:36 -…
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Chapters (17)
Introduction: Why RAG is the Enterprise Standard
0:56
Q1: What is RAG & Why is it critical for Enterprise AI?
1:48
Q2: Core Components: Retriever vs. Generator
2:41
Q3: Sparse vs. Dense vs. Hybrid Retrieval
3:30
Q4: Indexing Strategies: HNSW vs. IVF Trade-offs
4:36
Q5: Parent Document vs. Sentence Window Retrieval
5:26
Q6: Solving the "Lost in the Middle" Problem
6:13
Q7: Improving Zero-Shot Retrieval with HyDE
7:02
Q8: Pre-retrieval Query Optimization Techniques
8:00
Q9: Standard RAG vs. Corrective (CRAG) vs. Self-RAG
8:50
Q10: What is Agentic RAG?
9:36
Q11: Graph RAG vs. Traditional Vector RAG
10:25
Q12: Do Long Context Models Make RAG Obsolete?
11:11
Q13: How to Evaluate RAG Systems (RAGAS & Metrics)
12:00
Q14: Optimizing for Accuracy and Latency
12:56
Q15: Red Flags in RAG System Design
13:46
Conclusion: Mastering the AI Masterclass
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