#rag
18 articles
01
·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.
02
·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.
03
·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.
04
·Audio & Speech·★ MEMBER·PAPER·14 min read
Paper Walkthrough: VoiceMem — A Left Brain and a Right Brain for Voice Agents, at Zero Added Latency
A from-scratch walkthrough of VoiceMem, a memory system for real-time speech interaction. A factual 'left brain' and an affective 'right brain' run in parallel, and the whole retrieval is hidden inside the silence a VAD already waits out — which is how it wins at a top-5 budget.
05
·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.
06
·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.
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
·Large Language Models·FREE·PAPER·9 min read
Why Language Models Hallucinate — The Mechanics and What Actually Helps
Confident fabrication isn't a bug — it falls straight out of next-token prediction. This piece takes the mechanism apart layer by layer: no truth term in the loss, lossy compression that fails at the edges, sampling that draws from the tail, and scoring rules that pay models to guess. Then it ranks the countermeasures that actually work: grounding, mechanical citation checking, constrained output, and uncertainty estimation.
09
·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.
10
·Agents·★ MEMBER·PAPER·11 min read
Designing Agent Memory — Short-Term, Long-Term, Episodic
An LLM remembers nothing. Conversations only look continuous because you re-send the whole history every turn. Building up from zero: the three-layer split, the function that decides what gets recalled, and the part almost nobody designs — forgetting.
11
·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.
12
·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.
13
·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.
14
·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.
15
·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.
16
·Paper Deep-Dives·★ MEMBER·PAPER·8 min read
Paper Deep Dive: AskChem — Changing the Unit of Search from Papers to Provenance-Carrying Claims
An NYU-led systems paper that swaps the retrieval unit of chemistry literature search from papers to claims grounded by a DOI and a verbatim quote. We walk through the 2.4M-claim index, evidence graph, and hybrid search design, the evaluation that took DOI resolution from 88.3% to 100%, and the paper's honest limitations.
17
·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.
18
·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.