Retrieval-Augmented Generation for Large Language Models: A Survey (Gao et al., 2023)
What it covers
Comprehensive technical survey of RAG systems - the core technology powering AI search. Covers Naive RAG, Advanced RAG, and Modular RAG paradigms. Explains retrieval, generation, and augmentation techniques. Details evaluation frameworks, benchmarks (RGB, RECALL, RAGAS, ARES, TruLens), and implementation approaches. Addresses challenges: context length, robustness, and dealing with adversarial information.
Why essential
Understanding RAG is fundamental to understanding how AI search actually works technically. B2B tech companies need this foundational knowledge to optimize content retrieval.