This article details five distinct Retrieval-Augmented Generation (RAG) architectures, emphasizing that they are not competing solutions but rather layers that can be progressively combined. The core problem RAG addresses is providing language models with timely, relevant knowledge not present in their training data. The simplest architecture, Naive RAG, involves indexing documents into a vector database and performing a similarity search at query time to retrieve relevant chunks for the LLM. AI
IMPACT Provides a practical guide for developers building AI systems that require access to external knowledge bases.
RANK_REASON Article details specific technical architectures and tools for implementing RAG systems.
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