RAG Engineering Guides: Architecture, Vector Databases, Query Routing, and Knowledge Ops
6 posts in this series
This series is for turning RAG from a prototype into a system you can reason about. It covers architecture, retrieval quality, vector database selection, routing, and knowledge governance for agentic company brain scenarios.
RAG + Agent: Next-Generation AI Application Architecture
Architecture evolution from traditional RAG to Agentic RAG, with detailed comparison of 10 RAG patterns, framework selection guide, enterprise implementation roadmap, and intelligent customer service case study
RAG Vector Database Selection: Pinecone vs Weaviate vs Milvus Deep Comparison
RAG vector database selection guide: Deep comparison of Pinecone, Weaviate, and Milvus architecture, performance, pricing, and use cases. Includes LangChain integration code and real cost calculation formulas to help you choose the right retrieval engine for your AI application.
RAG System Optimization: Balancing Retrieval Precision and Generation Quality
Struggling with inaccurate RAG retrieval? This guide systematically covers Query processing, hybrid search, reranking, chunking strategies, and evaluation loops—with a decision framework to balance precision and latency.
RAG Query Routing in Practice: Multi-Vector Store Coordination and Intelligent Retrieval Distribution
RAG query routing in practice: A systematic comparison of three approaches—logical routing, semantic routing, and EnsembleRetriever—with complete LangChain code implementations, including cost optimization strategies like Semantic Caching and Tiered Retrieval.
RAG Query Routing in Practice: Multi-Vector Store Coordination and Intelligent Retrieval Distribution
A practical guide to RAG query routing: how to implement multi-vector store coordinated retrieval using EnsembleRetriever and Semantic Router. From logical routing to semantic routing, to RRF algorithm merging, with complete code examples and performance comparisons.
Hyper's Company Brain: How Should an AI Agent Knowledge Base Be Designed?
Using Hyper's public launch details, this guide explains how an AI agent company knowledge base should handle facts, permissions, retrieval, hooks, MCP, and human correction, with a 7-day pilot plan for small teams.
