Modular RAG Architecture: A Business Decision Guide

Q: What is modular RAG architecture?

Modular RAG decomposes Retrieval-Augmented Generation into interchangeable components — retriever, reranker, generator, and orchestrator — allowing teams to swap, upgrade, or customize each part independently.

Q: Why is RAG architecture a business decision?

RAG architecture choices directly impact accuracy, latency, cost, and data governance. Choosing between naive RAG, advanced RAG, and modular RAG affects product quality, operational costs, and compliance posture.

Q: What is the difference between naive RAG and modular RAG?

Naive RAG uses a simple retrieve-then-generate pipeline. Modular RAG adds query transformation, multi-source retrieval, reranking, and iterative refinement as composable modules that can be independently optimized.

Q: How do you choose a vector database for RAG?

Evaluate based on: scale (millions vs billions of vectors), latency requirements, filtering capabilities, managed vs self-hosted preference, and cost. Popular choices include Pinecone, Weaviate, Qdrant, and pgvector.

Q: Can RAG replace fine-tuning?

RAG and fine-tuning solve different problems. RAG is best for factual accuracy with dynamic data. Fine-tuning is better for style, format, and domain-specific reasoning. Many production systems combine both approaches.

Satyam Kumar

ブログに戻る

RAG & Retrieval Systems

Modular RAG: Why the Architecture of Retrieval Is Now a Business Decision

March 6, 202630 min read

modular RAG RAG architecture retrieval augmented generation enterprise RAG RAG patterns AI retrieval

Frequently Asked Questions

この記事を共有する

Twitter LinkedIn WhatsApp

Satyam

AI＆クラウドアーキテクト。数百万人にスケールするシステム構築を支援。

Modular RAG: Why the Architecture of Retrieval Is Now a Business Decision

Frequently Asked Questions

この記事を共有する

Comments

Leave a comment