RAG Implementation Patterns: 7 Production Architectures
Comparison of RAG patterns — naive, advanced, multi-modal, graph-based, and hybrid — with code examples and benchmarks.
Key Highlights
- ✓ 7 RAG architectures compared
- ✓ Code examples in Python
- ✓ Latency and accuracy benchmarks
- ✓ Vector database selection guide
- ✓ Production deployment patterns
Overview
A practical comparison of 7 Retrieval-Augmented Generation (RAG) architectures, from simple semantic search to advanced graph-based and multi-modal approaches. Includes code examples, latency benchmarks, and production deployment guidance.
What's Inside
Naive RAG
The simplest RAG pattern: embed documents, store in a vector database, retrieve top-k results, and append to the prompt. We cover embedding model selection, chunk size optimization, and the limitations that motivate more advanced patterns.
Advanced RAG with Reranking
Adding a reranking stage (using cross-encoders or LLM-as-judge) significantly improves retrieval precision. We benchmark ColBERT, Cohere Rerank, and custom reranking approaches, showing 20-40% improvement in answer quality.
Graph-Based RAG
Knowledge graph-enhanced RAG combines vector similarity with structured relationship traversal. We demonstrate how GraphRAG and similar approaches excel at multi-hop reasoning and entity-centric queries where traditional RAG fails.
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