Introduction When designing a RAG (Retrieval-Augmented Generation) system, understanding...
Introduction
When designing a RAG (Retrieval-Augmented Generation) system, understanding what's happening inside vector search lets you tune the trade-offs between accuracy, speed, and cost yourself.
This article covers the fundamentals of vector search:
ANN search (Approximate Nearest Neighbor) and HNSW (Hierarchical Navigable Small World)
Similarity and distance metrics (cosine similarity, dot product, L2 distance)
A practical RAG database design guide covering SQL, BM25, vector search, reranking, security, cost, and locality-aware context…
The first two parts of this series covered why production RAG systems fail and how the quality of the...
Context graphs traverse entity relationships ANN search can't follow. Learn when to use graphs, vectors, or both in your RAG…
A short learning path from a weekend project: I indexed my personal markdown notes (~800 chunks),...
I Built a Vector Search Engine from Scratch — Here's What I Learned Implementing HNSW...
How metadata filtering, tenant scope, and data locality reduce the vector search space before ranking in a RAG database.
