Mastering RAG: Chunking Deep Dive: From Fixed-Size Splitting to Contextual Retrieval - The Empirical Guide to RAG's Most Underrated Bottleneck

By YOO BYUNG HONG

The One Chunking Book That Actually Ran the Benchmarks

Your RAG accuracy is stuck at 62%. You've tried better embeddings, bigger models, prompt tricks. Nothing moved the needle. The culprit? Chunking - the least glamorous, most impactful part of RAG. This is the deep-dive book on the ONE thing that decided +26% Recall in the author's production system.

Why an Entire Book on Chunking

Because the "just use RecursiveCharacterTextSplitter(1000, 200)" advice is why your retrieval breaks on real documents. This volume, Book 1 of the Mastering RAG series, is 21 chapters and a real 50,000-chunk benchmark dedicated to answering one question: which chunking method actually wins, and why?

The 8 Chunking Methods, ComparedFixed-size: the baseline everyone starts with - and its blind spotsRecursive character: LangChain's default, its hidden trade-offsSentence & semantic: KSS, spaCy, KoSimCSE percentile splittingSmall-to-Big & propositional: retrieve fine, return coarseContextual Retrieval (Anthropic): full implementation with prompt cachingAgentic chunking: LLM as editor, when it's worth the costLate Chunking (Jina): reversing the order for long-context embeddingsHybrid strategies: which combinations actually winThe Empirical Experiment 50,000 chunks from a real production project (not toy Wikipedia data)15-query golden set: facts, procedures, comparisons, overviewsRecall@1/5/10, MRR, per-query-type breakdownsCost analysis: Contextual Retrieval at $47 total for +26% RecallWhy Semantic Chunking underperformed in practice - with root causeBaseline reproducibility issues you won't see in blog postsHistory and EvolutionPre-BERT IR era: why chunking didn't existBERT's 512-token ceiling: how chunking became mandatoryLangChain's standardization era and its lock-in effectsSemantic chunking emergence, then LLM-based chunkingWho This Book Is ForEngineers whose RAG plateaued and don't know whyTech leads deciding between chunking libraries and strategiesML engineers who need to justify chunking choices with numbersAnyone who read "Advanced RAG" and wants the full chunking storyWhat Makes This Book Different

Every chunking blog post says "it depends." This book shows you what it depends on - with a benchmark, a golden set, and a $47 invoice. Python and Java implementations included, along with a production deployment checklist and appendix cheat sheets covering every major chunking library.

The Mastering RAG Series

Volume 1: Chunking Deep Dive (this book) - Volume 2: Retrieval Deep Dive (BM25, hybrid, rerank) - Volume 3: Embeddings and Production Stack. Each volume goes as deep on one topic as most books go across all of RAG.

Prerequisites

Basic RAG knowledge (or read "AI RAG for Web Developers" first). Comfortable with Python; Java examples optional.

Stop guessing at chunk sizes. Read the benchmark, ship the answer.

• Format: Paperback
• Language: English
• ISBN: B0HCJVN1JY
• ISBN13: 9798190211887
• Release Date: August 2026
• Publisher: Independently Published
• Length: 140 Pages
• Weight: 0.43 lbs.
• Dimensions: 0.3" x 6.0" x 9.0"