Skip to content

Welcome

Context Graph: How Organizations Use LLMs Cost Effectively

Welcome to Context Graph: How Organizations Use LLMs Cost Effectively.

About This Book

Large organizations want to use large language models (LLMs) to answer questions and generate accurate content, but the models themselves know nothing about the organization. The missing piece is context — the right slice of enterprise knowledge, delivered into the prompt at the right moment, in the fewest tokens.

This book is about context graphs: enterprise graph data structures designed to assemble that compact, high-value context. Context graphs are structured, persistent records of product data, customer data, ontologies, and decision traces — capturing not just what happened inside an enterprise, but why it happened, who approved it, and which precedents justified it.

The focus throughout the book is token efficiency and quality of content returned from an LLM. Every modeling decision, retrieval pattern, and architectural choice is evaluated against that pair of constraints.

Who This Book Is For

This textbook is written for three overlapping groups:

  • Enterprise architects and senior engineers designing AI-powered systems who need a principled approach to organizational memory and context management.
  • AI/ML practitioners and data engineers building LLM-powered applications and struggling with hallucinations, missing context, and poor decision quality in agent workflows.
  • Technical product managers and founders building or evaluating products in the enterprise AI space who want a framework for where context graphs create durable competitive advantage.

The book assumes comfort reading technical content and some exposure to software systems. It does not require a deep background in machine learning or graph databases — knowledge graphs, labeled property graphs, and formal ontologies are all introduced from first principles.

How to Use This Book

Use the navigation menu on the left to explore:

  • Chapters — the main educational content, 22 chapters in dependency order from knowledge graphs through context graph deployment.
  • Learning Graph — an interactive concept visualization showing how every idea in the book depends on the ones before it.
  • MicroSims — interactive simulations for hands-on learning, embeddable as iframes inside any chapter.
  • Course Description — the seed document and Bloom's-taxonomy learning outcomes for the whole book.
  • About — audience, prerequisites, and how to read the book.

Getting Started

Start with Chapter 1: Knowledge Graphs and LPGs to build the graph-modeling foundation the rest of the book stands on.