RAG ARCHITECTURE

Effective RAG strategies for LLM applications & AI agents

This paper explores the intricacies of Retrieval-Augmented Generation (RAG) strategies, emphasizing the superiority of semantic RAG for enterprise software architects aiming to build robust generative AI applications and agents. It also provides an enterprise-ready RAG architecture diagram.
Effective RAG strategies for LLMs
WHAT'S INSIDE

Why semantic RAG is better for enterprise applications

Semantic RAG, also known as “advanced RAG”, introduces semantic understanding into the retrieval process
accuracy
Precision and relevance

By understanding the semantics of the query and the content, the retrieval system can fetch more relevant information

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Enhanced user experience

By ensuring that the responses are both accurate and contextually coherent, semantic RAG enhances the overall user experience

scalability
Scalability

Semantic RAG, with its use of advanced retrieval techniques, offers a scalable solution that can efficiently handle large knowledge bases

vector-indexing
Adaptability

Semantic RAG is highly adaptable to various enterprise use cases, making it a versatile tool for generative AI applications and agents

WHY READ THIS GUIDE?
Enterprise architects and software developers should read this guide to: 
  • Understand Retrieval-Augmented Generation (RAG)
  • Discover the challenges of basic RAG strategies vs the advantages of semantic RAG
  • Learn how RAG helps prevent LLM hallucinations
  • Explore why semantic RAG is the best strategy for enterprise teams
  • Get an enterprise-ready RAG architecture diagram
  • Review an example use case for semantic RAG