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About This Book

Welcome from Nexus

Hello, graph builders. I'm Nexus, and I'll be your guide through this textbook. Context graphs are a new kind of enterprise structure — part knowledge graph, part memory of the why behind every decision your organization makes. Together we'll trace those decisions across systems, build precise graph schemas you can actually query, and learn how to fit them into an LLM's context window for the lowest possible cost. Let's trace the why!

Why This Intelligent Textbook

The most expensive problem in enterprise AI is no longer the model — it is the context the model is given. Large language models can summarize, reason, and draft fluent text, but only when they are handed the right organizational knowledge at the right moment. Today most organizations solve that problem one prompt at a time, stuffing whatever they can fit into a context window and hoping for the best. There is no canonical reference for the discipline that should sit underneath those prompts. This book exists to define that discipline and to give practitioners a working blueprint.

The opportunity is genuinely large:

  • McKinsey estimates generative AI could add $2.6 to $4.4 trillion in value annually across industries — but the majority of enterprise deployments remain stuck in pilot stages, blocked by hallucinations, missing context, and brittle integrations1.
  • Foundation Capital's analysis of the AI market frames the trillion-dollar enterprise opportunity not as building better foundation models, but as solving the context problem — giving models the right organizational knowledge at the moment of decision2.
  • Gartner predicts that over 40% of agentic AI projects will be cancelled by the end of 2027, citing escalating costs, unclear business value, and inadequate risk controls — failure modes that largely trace back to missing or unreliable context3.
  • The Stanford AI Index reports that enterprise generative AI adoption has crossed a majority threshold, yet only a small fraction of organizations report consistent, production-grade results — a gap that reflects the missing layer of persistent, structured context4.

The literature gap is the reason for this book:

Context graphs as a discipline sit between three mature fields — knowledge graphs, retrieval-augmented generation (RAG), and process mining — but no widely available textbook treats them as a unified practice. Practitioners building this layer today usually stitch together blog posts, vendor documentation, and trial-and-error. We believe context graphs deserve a foundational text, and we hope this book becomes a pillar reference that future books, courses, and products build on.

What makes this book different:

Most books on enterprise AI stop at architectural sketches or vendor walkthroughs. This one is built on a validated learning graph of 496 interconnected concepts organized into 12 taxonomy categories, introduced across 22 chapters in strict prerequisite order so the ideas compound rather than collide. It pairs an extensive background on graph fundamentals, semantic layers, metadata standards, and decision traces with precise, queryable graph models — schemas you can implement and query directly, not just diagrams to admire. Throughout the book you will find interactive MicroSims that let you manipulate context-graph behavior in the browser. The entire textbook is open source and free — no paywalls, no access codes, no subscription — because a foundational discipline needs an accessible foundational reference.

How to Use This Book

This textbook is designed for self-paced study. Each chapter builds on previous material, so reading in order is recommended. The book includes:

  • 22 Chapters covering graph fundamentals, enterprise knowledge graphs, semantic layers, metadata standards, process mining, the context problem, decision traces, graph data modeling, LLM integration, AI agent architecture, enterprise use cases, compliance and audit, market strategy, and organizational adoption.
  • Interactive MicroSims embedded in chapters — browser-based simulations you can manipulate to explore concepts.
  • Annotated References linking to authoritative sources at the end of each chapter.
  • Learning Graph visualizing 496 concepts and their dependencies across 12 taxonomy categories.
  • Pedagogical Mascot — Nexus the Spider — who appears throughout the book to flag key insights, warnings, and celebrations.
  • Search available from any page using the search bar.

The Learning Graph visualizes how concepts connect across chapters. If you want to explore non-linearly or check prerequisites for a specific topic, start there. Start the linear reading path with Chapter 1: Knowledge Graphs and LPGs.

About the Author

Dan McCreary is a semi-retired AI researcher, solution architect, and educator who has spent more than three decades helping Fortune 100 organizations reason over massive datasets. At Optum he founded the Generative AI Center of Excellence and led the team that built one of the world's largest healthcare knowledge graphs — spanning over 25 billion vertices — to unify member, provider, and patient insights. Dan's deep background in knowledge representation and systems thinking underpins the precise learning graphs and intelligent textbook workflows used throughout this book.

He is the co-author of Making Sense of NoSQL (Manning Publications), the founding chair of the NoSQL Now! conference, and a frequent keynote speaker on semantic search, ontology strategy, and AI hardware. Beyond industry, Dan has mentored students as a STEM volunteer since 2014 and now applies the same rigor to building open educational resources. You can visit the Intelligent Textbooks Case Studies to see over 87 textbooks that Dan has created or co-created with other authors.

Selected Credentials

  • B.A. in Physics and Computer Science from Carleton College
  • M.S.E.E. from the University of Minnesota
  • MBA coursework at the University of St. Thomas
  • Patent holder in semantic search and ontology management techniques
  • Advocate for large-scale Enterprise Knowledge Graph adoption across healthcare and education
  • Long-time promoter of accessible, low-cost AI-powered learning experiences

How to Cite This Book

If you reference this textbook in academic work, technical papers, internal architecture documents, or other publications, please use one of the following citation formats.

APA (7th edition)

McCreary, D. (2026). Context Graph: How Organizations Use LLMs Cost Effectively. https://dmccreary.github.io/context-graph/

Chicago (17th edition)

McCreary, Dan. 2026. Context Graph: How Organizations Use LLMs Cost Effectively. https://dmccreary.github.io/context-graph/.

MLA (9th edition)

McCreary, Dan. Context Graph: How Organizations Use LLMs Cost Effectively. 2026, dmccreary.github.io/context-graph/.

BibTeX

@book{mccreary2026contextgraph,
  title     = {Context Graph: How Organizations Use LLMs Cost Effectively},
  author    = {McCreary, Dan},
  year      = {2026},
  url       = {https://dmccreary.github.io/context-graph/},
  note      = {Interactive intelligent textbook}
}

To cite a specific chapter, append the chapter number and title — for example:

McCreary, D. (2026). Chapter 1: Knowledge Graphs and LPGs. In Context Graph: How Organizations Use LLMs Cost Effectively. https://dmccreary.github.io/context-graph/chapters/01-knowledge-graphs-lpg/

License

This work is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). You are free to share and adapt the material for non-commercial purposes as long as you give appropriate credit and share your adaptations under the same license.

References

  1. McKinsey & Company. (2023). The economic potential of generative AI: The next productivity frontier. https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier ↩

  2. Foundation Capital. (2024). AI's Trillion-Dollar Opportunity: The Service-as-Software Era. https://foundationcapital.com/ai-service-as-software/ ↩

  3. Gartner. (2025). Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027. https://www.gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027 ↩

  4. Stanford Institute for Human-Centered Artificial Intelligence (HAI). (2025). Artificial Intelligence Index Report 2025. https://aiindex.stanford.edu/report/ ↩