RAG Interview Questions & Answers (2026)
A large collection of 1297 interview questions and answers on Retrieval-Augmented Generation, covering 52 RAG architectures, system design scenarios, and production failure modes for AI/ML engineers.
This repository is an interview-preparation resource for AI engineers, ML engineers, and GenAI/LLM developers, holding 1297 questions and answers about Retrieval-Augmented Generation, the LLM architecture that grounds model responses in external knowledge by chunking, embedding, and storing documents in a vector database, then retrieving relevant chunks at query time to pass to the model as context. It covers all 52 RAG architectures the project tracks, system design scenarios, vector databases, embeddings, chunking, reranking, evaluation, and production failure modes that come up in real LLM engineering interviews.
Included materials are a cheatsheet comparing all 52 RAG types in one table, an interactive quiz site presenting the questions as filterable flashcards, a structured learning path for longer preparation, and runnable lab notebooks, including one that can be run in Google Colab with no local setup, or run locally after installing requirements and setting an AI provider, model, and API key in an environment file. It targets AI/ML engineers preparing for RAG or GenAI interview rounds, software engineers moving into LLM application development, and data scientists facing RAG system design interviews.