Retrieval-Augmented Generation (RAG) Interview Questions and Answers Hub is a curated collection of over 100 interview-style questions designed to assess understanding of Retrieval-Augmented Generation (RAG) systems. It targets LLM engineers, researchers, and candidates preparing for technical roles, offering a structured pathway to master RAG concepts, implementation details, and evaluation methodologies. The repository aims to provide a comprehensive resource for anyone needing to understand the intricacies of RAG for interviews or practical system design.

How It Works

This repository functions as a knowledge base, organizing RAG-related interview questions into thematic categories such as indexing, retrieval, re-ranking, and evaluation. It covers fundamental RAG principles, challenges, and benefits, alongside advanced techniques like query transformation, chunking strategies, embedding models, vector databases, and various re-ranking approaches. The Q&A format is intended to clarify complex topics, from basic RAG mechanics to nuanced evaluation metrics like Context Precision, Faithfulness, and Response Relevancy.

Highlighted Details

• Extensive coverage of RAG topics, featuring over 100 questions on fundamentals, indexing, retrieval, re-ranking, and evaluation.
• Detailed exploration of RAG-specific evaluation metrics (Context Precision, Context Recall, Faithfulness, Response Relevancy) alongside traditional information retrieval metrics.
• Covers advanced RAG techniques including query transformation, chunking strategies, embedding models, quantization, and different re-ranker models.
• Provides links to related repositories for LLM, prompt engineering, and survey papers, fostering a broader learning ecosystem.

Maintenance & Community

The repository is maintained by KalyanKS-NLP. Community engagement is encouraged through a request for GitHub stars. No specific community channels (e.g., Discord, Slack) or detailed roadmap information are provided in the README.

Licensing & Compatibility

No license information is specified in the provided README content.

Limitations & Caveats

The "Answer" column for each question indicates "link," suggesting that detailed answers may be external or require further navigation, rather than being directly embedded. This repository is purely a Q&A collection and does not include code, implementations, datasets, or performance benchmarks for RAG systems. Information regarding maintenance frequency, contribution guidelines, or a project roadmap is absent.