Stop Treating Chunking as a Default Setting. Master the Core Architecture of High-Accuracy RAG.

Almost every retrieval failure in production Retrieval-Augmented Generation (RAG) systems is a chunking failure wearing a disguise.

When an LLM hallucinates, misses key facts, or cites the wrong section of a policy, the typical response is to upgrade the model or tune the ranker. But if your ingestion pipeline severed a crucial condition from its obligation, split a person's name from their action, or stripped the headers from a financial table, no downstream model or reranker can recover the lost information.

Chunking Strategies for LLM & RAG Systems is the definitive, system-design-level guide to document segmentation, boundary mechanics, and retrieval engineering in enterprise AI pipelines.

What You Will Learn

• The Mechanics of Information Loss: Understand why naive fixed-size character and token splitters destroy relational context, pronoun antecedents, and modifier bounds.
• Recursive & Structure-Aware Splitting: Build hierarchical splitting engines that respect document semantics across Markdown, HTML, and PDF architectures.
• Semantic Chunking with Cosine Distance: Dynamically detect topic transitions and statistical breakpoints using sentence embedding trajectories.
• Specialized Document Parsers: Engineer robust pipelines for complex data types including legal contracts, AST-based source code, multi-column tables, and email threads.
• Advanced RAG Architectures: Implement Parent-Child (Small-to-Big) retrieval, contextual chunk prefixing, proposition extraction, and late chunking.
• Hybrid Retrieval & Metadata Lineage: Combine Dense semantic vectors and BM25 sparse lexical search via Reciprocal Rank Fusion (RRF) with metadata pre-filtering.
• Size Optimization & Token Budgeting: Balance retrieval precision against contextual recall using empirical parameter sweeps and context-window math.
• Production Hardening & Evaluation: Implement SHA-256 deduplication, versioned chunk registries, and measure performance using Recall@K, MRR@K, nDCG@K, and answer faithfulness.

Who This Book Is For

• AI Engineers & Machine Learning Practitioners building, debugging, and scaling production RAG systems.
• Software Architects & Technical Leads evaluating vector databases, ingestion pipelines, and enterprise knowledge architectures.
• Candidates Preparing for AI System Design Interviews seeking deep architectural rigor on document ingestion and retrieval trade-offs.
• Companion Interactive Laboratory
• Every strategy in this book is backed by runnable, local code in the open-source companion project chunking-llm-lab. Experience an interactive 10-stage pipeline visualizer, in-situ document boundary overlays, and an automated test harness designed to run locally with zero API keys required.