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📘 BOOK OUTLINE

Programmable Intelligence

How to Build AI Systems With Markdown

By Mark Kendall

INTRODUCTION — The Shift No One Saw Coming

• Why AI is not a chatbot — it’s a programmable cognitive engine

• How markdown became the new source code

• Why the world misunderstood “prompt engineering”

• The moment I realized the AI responds to frameworks like a CPU responds to instructions

• The origin story of LearnTeachMaster and the birth of interactive cognitive scaffolding

• Why this book matters now

PART I — The Foundations of Programmable Intelligence

Chapter 1 — AI Is Not What You Think It Is

• The difference between “chat” and “instruction execution”

• Why AI behaves like a virtual machine with no code syntax

• Why talking to LLMs feels like talking to a person—but is actually programming

Chapter 2 — Markdown as the New Programmable Medium

• Why markdown works better than plain text

• Code blocks, sections, headers = behavioral compartments

• The psychological and computational reason LLMs love structure

• Markdown as the “assembly language” for cognitive systems

Chapter 3 — Cognitive Architecture Inside an AI

• The 5-layer mental architecture of an LLM

• How roles, constraints, goals, and examples create a temporary brain

• Why AI keeps “state” even without memory

• How a pasted markdown file becomes a controlled mindspace

Chapter 4 — Why Prompt Engineering Is Misunderstood

• The hype vs the reality

• Why 99% of prompt courses teach linguistic massaging, not engineering

• The dangerous misconception: “prompts are just magic words”

• What real prompt engineering actually is: instruction system design

PART II — The Mechanics of Programmable Intelligence

Chapter 5 — The Eight Framework Engines

Breakdown of the eight frameworks (RTF, SOLVE, TAG, RACE, DREAM, PACT, CARE, RISE) as mental modules that structure AI reasoning.

For each:

• What it does cognitively

• Why LLMs respond to it

• How it modifies the AI’s internal model

• When to use it

• Real-world engineering examples

Chapter 6 — Building Personas and Roles

• Why “Act as a…” is not a gimmick but a compiler instruction

• The difference between persona, capability, and domain

• How roles stack and how to avoid role collision

• Building multi-persona systems

• Example personas: senior engineer, architect, business strategist, musician, therapist

Chapter 7 — Chaining: The Hidden Superpower

• Why one prompt isn’t enough

• Creating multi-stage systems

• State management across replies

• Using markdown to build “reusable cognition modules”

• The difference between task chaining and cognitive chaining

• LearnTeachMaster as a case study

Chapter 8 — The AI Operating System Model

• Every markdown file = a program loaded into the AI’s memory

• Using sections as “subsystems”

• How to create:

  
  

  • kernel (persona/role)

  • shell (interaction rules)

  • modules (frameworks)

  • applications (tasks)

• 
  

• How to reboot, refresh, or reinitialize the AI

PART III — Applied Programmable Intelligence

Chapter 9 — Building Your First AI System

A guided walkthrough:

• Define the role

• Add context

• Insert a framework

• Plug in examples

• Set output expectations

• Add interactivity

• Make it self-referential

• Test and refine

Chapter 10 — Creating Interactive Learning Agents

• How to turn AI into a real teacher

• The psychology of interactive learning

• Step-by-step creation of LearnTeachMaster Interactive

• Why prompt systems outperform static documentation

• Using teach-back loops to deepen understanding

Chapter 11 — Agents for Engineering

• The engineer’s AI copilot blueprint

• Architecting systems

• Generating code

• Debugging

• Diagramming

• Knowledge extraction from repos

• Chain-of-thought lanterning for complex problems

• How engineers save 20–50 hours per week with the right scaffolding

Chapter 12 — AI for Architecture and Design

• Turning business goals into architecture diagrams

• Converting blurry requirements into clean blueprints

• Event-driven design through AI

• Creating reusable design patterns

• AI as a systems architect

Chapter 13 — AI for Creative Work

• Songwriting

• Music scoring

• Fiction / storytelling

• Visual art and moodboards

• How creative chains differ from logical chains

Chapter 14 — AI for Business and Leadership

• Strategic decision frameworks

• Product ideation

• Team communication

• Presentations

• Executive summaries

• Building a whole “AI Chief of Staff”

PART IV — The Future of AI Engineering

Chapter 15 — The Rise of Cognitive Tooling

• AI as a universal interface

• Why markdown-level programming will become a standard skill

• The future role of prompt architects

• Multi-agent networks

• Self-organizing AI workflows

Chapter 16 — The New Engineering Career Model

• Why engineers who understand programmable intelligence will lead the next decade

• The end of rote coding; the rise of cognitive orchestration

• How companies will reorganize around AI copilots

• What makes an engineer irreplaceable in the AI era

Chapter 17 — From Prompts to Platforms

• How prompt frameworks evolve into full agent frameworks

• Why Prompt → Chain → Agent → System → Platform

• LearnTeachMaster as an enterprise blueprint

• Building your own AI-powered business

CONCLUSION — You Are Now an AI System Builder

• A final blueprint for building your own programmable intelligence

• The mindset shift that unlocks exponential capability

• Your invitation to the new era of engineering mastery

APPENDICES

• Appendix A: 50 reusable AI frameworks

• Appendix B: Starter markdown templates for engineers

• Appendix C: LearnTeachMaster reference doc

• Appendix D: Multi-agent architectural patterns

• Appendix E: Engineering mastery workflows