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

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RAG & Retrieval Systems

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

By Satyam KumarMarch 6, 202630 min read

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

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Satyam Kumar

Founder & AI Architect, AppScale LLP

AI & Cloud архитектор. Помогаю командам строить системы, масштабируемые до миллионов.

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

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