RAG & Retrieval
Retrieval-Augmented Generation (RAG) has become the dominant pattern for building AI applications that need to work with proprietary, recent, or domain-specific information. Almost every company building with LLMs uses some form of RAG.
RAG interview questions test your ability to design end-to-end systems that combine information retrieval with language model generation. Interviewers look for your understanding of the full pipeline — from document ingestion and chunking to embedding, vector search, and response generation — and your ability to reason about tradeoffs at each stage.
Key areas include: chunking strategies, embedding model selection, vector database trade-offs, hybrid search, re-ranking, and evaluating both retrieval and generation quality.
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RAG & Retrieval Interview Questions
When Would You Choose RAG Over Fine-Tuning?
Understand the tradeoffs between RAG and fine-tuning — and learn a decision framework for choosing the right approach for your use case.
Read questionHow Do You Handle Chunking Strategies for Different Document Types?
Compare chunking strategies for different document types — PDFs, code, HTML, and tables — and learn when each approach works best.
Read questionDesign a RAG Pipeline from Scratch
Walk through designing a production-ready RAG system covering document ingestion, chunking strategies, embedding models, vector search, and LLM generation.
Read questionHow Would You Evaluate Retrieval Quality in a RAG System?
Walk through metrics and methods for evaluating retrieval quality in a RAG pipeline — from offline metrics to end-to-end answer quality.
Read questionDesign a Hybrid Search System Combining Semantic and Keyword Search
Design a search system that combines dense vector search with sparse keyword search — outperforming either approach alone through intelligent score fusion.
Read questionPrep the coding round too
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