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

LLM Inference Optimization Part 4 — Production Serving
Production deployment with vLLM and TGI. Continuous Batching, Speculative Decoding, memory budget design, and throughput benchmarks.

LLM Inference Optimization Part 3 — Sparse Attention in Practice
Sliding Window, Sink Attention, DeepSeek DSA, IndexCache, and Nvidia DMS. From dynamic token selection to Needle-in-a-Haystack evaluation.

LLM Inference Optimization Part 2 — KV Cache Optimization
KV Cache quantization (int8/int4), PCA compression (KVTC), and PagedAttention (vLLM). Hands-on memory reduction code and scenario-based configuration guide.