RAG & Retrieval
Embeddings, chunking, reranking, evaluation
01
·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.
02
·RAG & Retrieval·FREE·8 min read
RAG vs Fine-Tuning — Which One, and When
The two main ways to make an LLM better, compared on four axes: knowledge freshness, cost, hallucination, and data requirements. From metaphor to math to interactive demos to the mistakes that break production systems.
03
·RAG & Retrieval·FREE·PAPER·6 min read
Embeddings from Scratch — from word2vec Intuition to Contextual Embeddings
Why does 'king − man + woman ≈ queen' actually work? A from-zero tour of embeddings: the map metaphor, the distributional hypothesis, the word2vec math, numpy code, contextual embeddings after BERT, and the knobs you touch in real RAG systems.
04
·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.
05
·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.
06
·RAG & Retrieval·★ MEMBER·PAPER·10 min read
GraphRAG from Scratch — Where Knowledge Graphs Meet Retrieval
Rebuild your documents as a web of entities and relationships and you can answer questions ordinary RAG cannot reach — the ones about the corpus as a whole. Extraction, entity resolution, community summarization, and local/global search from first principles, ending with an honest account of when it's overkill.
07
·RAG & Retrieval·★ MEMBER·PAPER·13 min read
Evaluating RAG in Practice — Turning “Seems Better” Into a Number
RAG improvement work gets lost because quality is judged as one vague feeling. This article builds up the metrics that separate retrieval from generation, the definitions of faithfulness and relevance, how to build an eval set with synthetic QA and where that goes wrong, and how many questions you need before the number means anything.
08
·RAG & Retrieval·★ MEMBER·PAPER·13 min read
Build Your Own Vector DB — From Brute Force to HNSW
Assemble a vector search engine step by step, starting from a 20-line brute-force scan. The curse of dimensionality, IVF partitioning, HNSW graph traversal and quantization, all viewed through one lens: the trade between recall and speed.
09
·RAG & Retrieval·★ MEMBER·PAPER·12 min read
Paper Walkthrough: WeMM-Embedding — Putting Text, Images and Video on One Ruler
A walkthrough of WeMM-Embedding (2B/4B/9B), Tencent's universal multimodal embedding family. The unified pair format, the <embedding> token, Matryoshka dimensions, two-stage training and distillation — explained from scratch, using only the numbers the paper reports.
10
·RAG & Retrieval·★ MEMBER·PAPER·13 min read
Paper walkthrough: Hi-Q — splitting a question down to the granularity your corpus can actually retrieve
The real bottleneck in multi-hop QA is a mismatch between the granularity of the question and the granularity of retrievable evidence. Hi-Q answers first, then expands only the nodes that failed — in dependency order. Here is the control rule, the tree, and the measured numbers, from zero background.