The Real Bottleneck in RAG Systems: It's Not the Vector DB, It's Your 1:N Relationships
Many teams try to solve RAG accuracy problems by tuning their vector database. But the real bottleneck is chunking that ignores the relational structure of source data.

Many teams try to solve RAG accuracy problems by tuning their vector database. But the real bottleneck is chunking that ignores the relational structure of source data. When you flatten customer-order-product 1:N:N relationships into independent chunks, no amount of vector DB optimization will prevent hallucinations.
This article covers how to properly integrate SQL relational data into RAG systems.
1. Why Vector DB Alone Isn't Enough
The Problem in Reality
When building a RAG system, you've probably received questions like:
Related Posts

Self-Evolving AI Agents — The New Paradigm of 2026
GenericAgent, Evolver, Open Agents — comparing 3 self-evolving agent frameworks that learn, adapt, and grow without human coding.

Build Your Own LLM Knowledge Base — A Karpathy-Style Knowledge System
Complete guide to building a permanent personal knowledge system with Obsidian + Claude Code. Wiki + Memory dual-axis architecture.

Why Karpathy's CLAUDE.md Got 48K Stars — And How to Write Your Own
One markdown file raised AI coding accuracy from 65% to 94%. Analyzing Karpathy's 4 rules and practical writing guide.