RAG approaches evolve from basic to agentic for enhanced LLM accuracy

Retrieval-Augmented Generation (RAG) is not a single architecture but a family of approaches designed for varying accuracy and complexity needs. Basic RAG involves chunking documents, creating embeddings, and retrieving top matches for an LLM. More advanced types include Hybrid RAG, which combines keyword and vector search; Reranking RAG, which uses a reranker model to improve relevance; Multi-Query RAG, which expands a single query into multiple variations; Graph RAG, which leverages knowledge graphs for connected context; Agentic RAG, where the LLM acts as an autonomous agent to plan and execute searches; and Self-RAG, which adds a verification layer after generation. AI

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IMPACT Understanding the nuances of RAG architectures is crucial for optimizing LLM performance and accuracy in real-world applications.

RANK_REASON The article details different types of Retrieval-Augmented Generation (RAG) techniques, which is a research topic in LLM applications. [lever_c_demoted from research: ic=1 ai=1.0]

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• RAG
• LLM
• Naive RAG
• Hybrid RAG
• Reranking RAG
• Multi-Query RAG
• Graph RAG
• Agentic RAG
• Self-RAG
• BM25
• GPT-4

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