A Developer's Guide to RAG Architectures
This article explores different types of Retrieval-Augmented Generation (RAG) architectures, their strengths, and when to use them for building LLM applications.
Why it matters
RAG architectures are critical for building production-ready LLM applications that can reliably access and reason over external knowledge.
Key Points
- 1Naive RAG is a basic setup for grounding LLMs, but has low precision and recall
- 2Advanced RAG optimizes retrieval, re-ranking, and query transformation for higher accuracy
- 3Modular RAG uses a composable architecture to handle complex data sources and queries
- 4Agentic RAG enables autonomous multi-hop reasoning and dynamic verification
Details
The article discusses four main types of RAG architectures: Naive RAG, Advanced RAG, Modular RAG, and Agentic RAG. Naive RAG is the simplest setup, but struggles with ambiguous queries and irrelevant retrieved context. Advanced RAG improves on this by optimizing pre-retrieval, retrieval, and post-retrieval stages to reduce noise and bridge the semantic gap. Modular RAG takes a composable approach, allowing dynamic selection of the right retrieval tool (SQL, vector search, APIs) for each query. Agentic RAG is the most sophisticated, treating the LLM as an autonomous agent that can plan, execute, and self-correct multi-step reasoning workflows. The article provides a comparison table and a decision framework to help developers choose the appropriate RAG architecture based on their use case complexity, latency requirements, and desired accuracy.
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