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 questionHow Do You Handle Tables, Charts, and Complex Documents in a RAG Pipeline?
Real-world documents contain tables, charts, and complex layouts that naive text extraction mangles. Walk through how to build a robust document processing pipeline for structured and visual content.
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 questionHow Do Vector Embeddings Work, and How Do You Choose the Right Embedding Model?
Explain what vector embeddings are, how embedding models convert text to vectors, and how you'd benchmark and improve retrieval accuracy for a production RAG system.
Read questionA Client's RAG System Has Poor Retrieval Accuracy — How Do You Fix It?
A RAG-based system isn't returning accurate results. Walk through a systematic process to diagnose the root cause and improve retrieval 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 questionHow Do You Handle Multi-Hop and Multifaceted Queries in a RAG System?
Single-shot retrieval breaks down for complex questions that require reasoning across multiple documents. Walk through strategies to handle multi-hop and multifaceted queries.
Read questionHow Do You Choose a Vector Index and Vector Database for a RAG System?
Compare vector index types — HNSW, IVF, PQ, LSH — and explain how to choose the right vector database given scale, latency, filtering, and cost requirements.
Read questionPrep for the full interview loop
Know the concepts. Now prove it. Practice GenAI, Coding, System Design, and AI/ML Design interviews with an AI that tells you exactly where you fell short.
Start a mock interviewFrequently Asked Questions
What is RAG and why is it tested in AI interviews?▾
RAG (Retrieval-Augmented Generation) combines a retrieval system — typically vector search — with a language model to generate responses grounded in external documents. It's tested heavily in AI interviews because it's the dominant pattern for building LLM applications that require accurate, up-to-date, or proprietary information, and because designing a robust RAG pipeline requires reasoning about tradeoffs across chunking, embeddings, retrieval, and generation.
What RAG topics come up most in AI engineer interviews?▾
The most common RAG interview topics are: chunking strategies and their impact on retrieval quality, embedding model selection, hybrid search (combining dense and sparse retrieval), re-ranking, RAG vs fine-tuning tradeoffs, evaluating retrieval and generation quality separately, and debugging poor RAG performance.
How do I explain a RAG pipeline in a system design interview?▾
Structure your answer in four stages: (1) Ingestion — document loading, chunking, and embedding; (2) Indexing — storing embeddings in a vector database; (3) Retrieval — query embedding, vector search, optional re-ranking; (4) Generation — constructing a prompt with retrieved context and calling the LLM. Then discuss tradeoffs: chunk size, embedding model choice, top-k retrieval, context window limits, and evaluation strategy.
What is the difference between RAG and fine-tuning?▾
RAG adds dynamic external knowledge at inference time without changing model weights — it's better for frequently updated information, proprietary data, and when you need source citations. Fine-tuning adapts model weights for a specific style, format, or domain — it's better for consistent behavior and tone changes. Most production systems use RAG for knowledge grounding and fine-tuning for output style.