LLM Primer III Enhancing Enterprise AI with RAG: A Practical Guide to Building Retrieval-Augmented Generation Systems for the Enterprise

RAG looks simple from the outside: embed your documents, retrieve relevant chunks, hand them to the model. Every layer of that pipeline is its own engineering discipline, and a wrong choice anywhere in the chain caps the quality of everything downstream.LLM Primer III is the practitioner's walkthrough of the full RAG stack, layer by layer: parsing, chunking, vector storage, retrieval, security, evaluation, and the continuous-update patterns that keep a system honest after launch. It is the book you reach for when the demo works and you have to ship the thing.What's insideThe four-stage evolution from Naive to Agentic RAG, and the honest answer to when fine-tuning beats retrievalLayout-aware document parsing, the chunking spectrum, and the Context CliffVector database trade-offs: managed leaders, OSS speed engines, Postgres extensions, and the residency questions that decide the real choiceHybrid retrieval with Reciprocal Rank Fusion, cross-encoder reranking, and HyDE-style query understandingThreat models specific to RAG, ACL/RBAC/ReBAC access patterns, and the embedding-leakage problem permission systems were never designed forDifferential-privacy techniques (DP-Prompt, DP-MLM, 1-Diffractor) and the utility-vs-privacy trade-off in practiceThe RAG Evaluation Triad — Context Relevance, Groundedness, Answer Relevance — and the rise (and limits) of LLM-as-a-JudgeContinuous indexing with CDC, semantic caching, model tiering, and feedback loops from production telemetryIncludes four reference appendices: math formulas, system prompts, decision matrices for vector databases and parsers and evaluation frameworks, and benchmark datasets for RAG evaluation.Written for engineers, technical product managers, and architects building enterprise RAG. Assumes general LLM familiarity at the level of Volume I. No advanced math required.Volume III of the LLM Primer series. Read more