RAG for LLMs
Free while signed in. Answers cite the passages they came from.
A broad survey of Retrieval-Augmented Generation research, organizing the rapidly growing literature into a coherent map.
Three-paradigm taxonomy: Organizes RAG approaches into Naive RAG, Advanced RAG (pre/post-retrieval enhancements), and Modular RAG (orchestrated component-based systems).
Core components: Reviews retrievers, generators, and augmentation strategies separately, clarifying which design choices sit in which component.
Evaluation and datasets: Catalogs RAG-specific benchmarks and evaluation metrics, surfacing the still-uneven state of RAG evaluation.
Frontier directions: Highlights agentic retrieval, multimodal RAG, and long-context RAG as the key research areas driving the 2024 RAG landscape.
Get next week’s papers.
The same picks and the same summaries, in your inbox. Free, and 176 issues deep.
Subscribe on Substack