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What is RAG? Why does chunking matter? What are embeddings used for in RAG? What is reranking? What can go wrong in a RAG pipeline? How do you evaluate retrieval quality in RAG? What is hybrid search in RAG? Why does metadata filtering matter in RAG? How do you build semantic similarity ranking? What distance metric? Cosine similarity vs Euclidean distance in embedding space? How do you evaluate Word2Vec embedding quality on a domain corpus? Faithfulness vs relevance in RAG evaluation? What is RAGAS? How would you integrate it into CI/CD? Walk me through the full RAG pipeline. What is semantic chunking? How does it differ from fixed-size? How do you design a retrieval layer for document extraction? Sparse retrieval (BM25) vs dense retrieval (ANN) — when to hybrid? How do you handle multi-document retrieval where context spans multiple files? What is reranking in RAG? When does it help? How do FAISS and ChromaDB differ? What is the lost-in-the-middle problem? How do you update a vector index when source documents change? What is parent-child chunking? How do you choose chunk size? How do you use metadata filtering to narrow retrieval? What is HyDE (Hypothetical Document Embedding)? How would you build a RAG system for thousands of portfolio documents? What is the role of embedding dimensionality and model choice in retrieval quality?

RAG Interview Questions

Retrieval-augmented generation concepts for grounded AI systems.

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