A Taxonomy of Retrieval Augmented Generation

In the rapidly advancing field of Retrieval Augmented Generation (RAG), it can be easy to feel overwhelmed by the sheer volume of technical jargon. To bridge this gap and make RAG more accessible, this comprehensive taxonomy provides a well-organized list of over 200 key terms, breaking down the ecosystem into 8 intuitive categories.

This taxonomy offers an accessible entry point for anyone looking to deepen their understanding of RAG, without being bogged down by unnecessary complexity. The terms are grouped into themes that cover the entire landscape of RAG, from core components to applied RAG patterns and the emerging RAGOps stack:

🔵 RAG Basics – Learn about LLM limitations, knowledge bases, and the principles of retrieval and generation.
🔵 Core Components – Understand indexing, chunking, metadata, embeddings, and retrieval strategies.
🔵 Evaluation – Explore key metrics like precision, recall, MRR, and frameworks like RAGAS and ARES.
🔵 Pipeline Design – Discover the design of naïve, advanced, and modular RAG systems.
🔵 Operations Stack – Learn about the layers that power RAG systems, including security, caching, and monitoring.
🔵 Emerging Patterns – Uncover innovations such as Knowledge Graph-powered RAG, multimodal retrieval, and agentic RAG.
🔵 Technology Providers – A comprehensive list of service providers offering tools for RAG development and deployment.
🔵 Applied RAG – Explore use cases, challenges, and real-world applications in content generation, customer support, and more.

Who can benefit from this taxonomy?

• Product managers looking to get a grip on RAG for AI-driven products
• Leaders tracking the latest trends in contextual AI
• ML Engineers and Data Scientists expanding their knowledge on RAG systems
• AI enthusiasts passionate about cutting-edge applications in generative AI

I'd also like to get your feedback on this taxonomy. Please feel free to get in touch

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