Skip to content

Chapters

This textbook is organized into 27 chapters covering 561 concepts.

Chapter Overview

  1. Core Concepts of Large Language Models - Introduces the vocabulary of generative AI systems — AI, machine learning, deep learning, transformers, and large language models — building the conceptual foundation every later cost discussion depends on.
  2. Prompting, Deployment, and Model Optimization Basics - Covers how prompts are engineered and models are deployed and optimized, from retrieval-augmented generation and hallucination through quantization and right-sizing.
  3. Tokenization and Token Pricing Fundamentals - Explains how text becomes tokens and how providers count and price them, establishing the vocabulary needed to read any generative AI usage bill.
  4. Managing and Benchmarking Token Consumption - Shows how prompt length, caching, chunking, and multi-turn conversations drive token consumption, and how to benchmark and compare token efficiency across providers.
  5. Compute Pricing and Scaling Fundamentals - Surveys the compute pricing models beneath every generative AI bill — on-demand, reserved, spot, and serverless — along with autoscaling and utilization tradeoffs.
  6. Infrastructure Planning and Cost Monitoring - Moves from serving infrastructure and hardware accelerators to capacity planning, multi-region deployment, and the dashboards used to monitor infrastructure spend.
  7. Prompt, Routing, and Retrieval Efficiency Techniques - Presents prompt optimization, model cascading and routing, and retrieval-tuning techniques that reduce token and compute cost without sacrificing quality.
  8. Model Compression, Testing, and Continuous Efficiency - Covers testing-driven efficiency techniques — A/B testing, benchmarking, distillation, quantization, and speculative decoding — plus the continuous-optimization discipline that sustains savings.
  9. Core Financial and ROI Vocabulary - Builds the core financial vocabulary a technologist needs before writing any ROI report: ROI, TCO, NPV, payback period, and basic cost-accounting terms.
  10. Business Case Development and Financial Forecasting - Extends financial fundamentals into unit economics, business-case development, investment appraisal, and the forecasting techniques used to project financial outcomes.
  11. Productivity and Quality Metrics for GenAI - Introduces the metrics that connect generative AI usage to productivity and quality outcomes, from adoption rate through developer velocity and defect reduction.
  12. Measurement Rigor and KPI Reporting - Covers accuracy-oriented metrics and the measurement rigor — baselines, control groups, statistical significance — needed to trust any reported KPI.
  13. Telemetry, Logging, and Cost Dashboards - Explains how usage and cost telemetry is captured through logging, tracing, and observability platforms, culminating in real-time cost dashboards.
  14. Cost Attribution, Governance, and Observability Maturity - Covers cost attribution across users, features, and departments, plus the data governance and instrumentation-maturity practices that keep cost data trustworthy.
  15. AI-Assisted Coding Across the SDLC - Traces cost impact across the software development lifecycle as AI assists with requirements, code generation, review, and testing.
  16. SDLC Productivity, Risk, and Cost Roadmapping - Covers developer-productivity metrics, the risks of AI-assisted coding, and the roadmapping techniques used to plan and report SDLC cost reduction.
  17. Data and Model Lifecycle Cost Drivers - Follows generative AI's cost impact through the data science lifecycle, from data collection and labeling through model training, evaluation, and drift detection.
  18. MLOps Infrastructure and Reuse Economics - Covers the MLOps infrastructure — feature stores, model registries, and deployment testing patterns — and reuse strategies that reduce data science cost.
  19. Vendor Pricing and Licensing Economics - Analyzes vendor and model pricing structures, licensing models, and the total-cost frameworks used to compare vendor offers on equal footing.
  20. Vendor Selection and Sourcing Strategy - Covers structured vendor-selection methods — benchmarking, pilots, due diligence, and scorecards — alongside the sourcing risks that accompany vendor decisions.
  21. Governance, Compliance, and Security Risk - Surfaces governance, compliance, and security risks that do not appear on a token invoice but materially affect a generative AI system's true cost.
  22. Risk-Adjusted Costing and Executive Risk Reporting - Covers risk-adjusted costing frameworks and the practices used to register, mitigate, and report AI-related risk to executive stakeholders.
  23. Audience Analysis and Executive Framing - Introduces the audience-analysis and message-framing skills needed to translate technical cost findings into terms a financial executive will act on.
  24. Presenting and Defending Findings to Executives - Covers presentation design, handling uncertainty and objections, and the credibility-building techniques used to defend findings before an executive audience.
  25. Designing the Executive ROI Report - Walks through the structural mechanics of an executive ROI report: scope definition, methodology, findings, recommendations, appendices, and front matter.
  26. Data Visualization and Storytelling for Financial Audiences - Covers chart selection and design for financial data — bar, line, waterfall, sensitivity, and scenario visualizations — built for a non-technical audience.
  27. Report Production, Review, and the Capstone Project - Combines the quality-assurance pass on a finished report with the step-by-step capstone project: building, critiquing, and presenting a complete GenAI ROI report.

How to Use This Textbook

Work through the chapters in order: each chapter's concepts depend only on concepts introduced in the same or an earlier chapter, so the numbering reflects a valid learning path. Readers with prior experience in a given area (e.g., generative AI foundations, or corporate finance) may skip ahead, but should verify prerequisite concepts from earlier chapters are already familiar.

Note: Each chapter includes a list of concepts covered. Make sure to complete prerequisites before moving to advanced chapters.