#vector-search
5 articles
01
·RAG & Retrieval·★ MEMBER·10 min read
Chunking Strategies — How You Split Decides What You Can Find
Most of a RAG system's quality is decided by how you split documents. The arithmetic of fixed-size chunks and overlap, structural and semantic splitting, parent-child chunks, and the two things that break every splitter: tables and equations.
02
·RAG & Retrieval·★ MEMBER·PAPER·10 min read
Recommenders and Embeddings — Same Math as RAG, Different Goal
What sits behind "recommended for you" is very nearly the same math as RAG's vector search. A from-zero tour: matrix factorization, two-tower models, and how the ANN stack is reused — plus why the evaluation and the failure modes end up completely different.
03
·Linear Algebra·FREE·7 min read
The Linear Algebra Under LoRA and RAG — Eigenvalues, Low Rank and Vector Search, Hands On
A matrix is a deformation of space, an eigenvector is a direction that survives it, SVD generalises the idea, and the dot product is the definition of 'similar'. Four interactive figures and four equations show that LoRA's ΔW=BA and RAG's vector search stand on the same floor. A column meant to be dragged, not just read.
04
·Data Structures·★ MEMBER·PAPER·9 min read
Hashing and Nearest-Neighbor Search — The Groundwork Under Vector Search
Two inventions that made looking things up fast — the exact-match hash table, and LSH and HNSW for searching by meaning — from zero assumed knowledge. What is actually running underneath RAG and every vector database.
05
·RAG & Retrieval·★ MEMBER·8 min read
RAG Fundamentals and Design Patterns — Embeddings, Chunking, Reranking, and Evaluation from Scratch
Retrieval-Augmented Generation explained from zero: the core intuition, chunking strategies, hybrid search and reranking, and the evaluation design that matters most.