Automating the Mundane: A Practitioner's System for Engineering Leverage with AI

This repository demonstrates a systematic approach to context engineering and AI integration that transforms scattered thoughts, emails, and documents into deterministic, high-value context for AI collaboration. It showcases the complete 4-Stage Context Engineering Pipeline and practical examples of systematizing professional workflows.

The Context Engineering Philosophy

Context is gold. Without good context engineering, prompt engineering is irrelevant.

This collection moves beyond random AI tool usage to demonstrate a deliberate, four-stage pipeline for manufacturing perfect, context-rich prompts:

1. Inception → Capture high-bandwidth thought as structured digital assets
2. Storage → Engineer a deterministic context repository, your "second brain"
3. Refinement → Synthesize raw data into actionable, strategic intelligence
4. Assembly → Orchestrate massive, holistic prompts for insurmountable leverage

The materials here demonstrate how AI transforms from a search tool into a strategic advisor through systematic context engineering.

Repository Structure and Contents

Engineering Principles Applied

This repository contains five practical demonstrations that illustrate engineering principles for AI integration:

• AI-Assisted Technical Analysis: Automating the Last Mile: View PDF
• AI-Powered Feedback Loop: Systematizing Self-Improvement: Call transcript analysis for communication improvement
• AI Vocabulary Coach: Extending Finite Expertise Infinitely: Norman Lewis + Tom Heehler principles applied to call analysis
• Automated CRM Enrichment: Systematizing Serendipity: Connection research and relationship mapping
• Modular Resume System: Documentation as Code: AI Consultant PDF | Tech Leadership PDF

The collection is organized into two primary sections: demos and prompts, demonstrating the complete context engineering workflow.

demos/

This directory contains implementations of the Context Engineering Pipeline, demonstrating professional document generation and workflow automation.

• demos/alpine-ai/: Complete context engineering system demonstrations
  • presentation/: The main presentation "Automating the Mundane" showcasing the 4-Stage Context Engineering Pipeline using Quarto and RevealJS
  • report-generation/: AI-Assisted Technical Analysis demo showing automated last-mile document production with LaTeX precision and programmatic TikZ diagrams
  • resume-system/: Modular Resume System implementing "Documentation as Code" principles with extensible LaTeX, source-of-truth data management, and AI-powered customization

prompts/

This directory demonstrates Stage 4: Context Assembly through meticulously engineered prompts that show how to orchestrate massive, holistic context for strategic AI collaboration.

• linguistics-prompts/: Implementation of the AI Vocabulary Coach system, including the "Heehler Method Vocabulary Coach" that extends finite expertise infinitely through call transcript analysis and vocabulary edge expansion
• osint/: Automated CRM Enrichment and systematic serendipity prompts, including "Individual Reputation Research" for connection research and context matching against ideal customer profiles

Types of Examples Included

The materials in this repository encompass a variety of formats and applications:

• Quarto Notebooks (.qmd): For reproducible research, dynamic reports, and interactive presentations.
• LaTeX Templates (.tex): For professional typography, custom document layouts, and high-quality PDF generation.
• Shell Scripts (.sh): For automating build processes, content compilation, and workflow orchestration.
• Markdown Files (.md): For detailed prompt engineering examples, documentation, and instructional content.
• HTML Exports: Generated from Quarto projects for web-based viewing of presentations and reports.
• PDF Outputs: Final, professionally formatted documents generated from Quarto and LaTeX sources.

Building Your Own Context Engineering System

You don't need permission to start building leverage. This repository provides the complete blueprint:

1. Start with Context Inception: Implement high-bandwidth thought capture (like Wispr Flow) to turn your thoughts into digital assets
2. Build Context Storage: Create your deterministic context repository using Git + Markdown rather than probabilistic MCP servers
3. Develop Context Refinement: Use agentic partners (Claude Code, Gemini CLI) to synthesize raw data into strategic intelligence
4. Master Context Assembly: Use tools like Prompt Tower to orchestrate massive, holistic prompts for complex decisions

To replicate the examples:

• Follow the build.sh scripts in demos/ to reproduce professional outputs
• Examine the .qmd files to understand the context engineering techniques
• Adapt the prompt templates in prompts/ for your specific use cases
• Fork and contribute your own systematized workflows

The Context Engineering Stack

This systematic approach leverages a deliberate technology stack:

Stage 1 - Context Inception:

• Wispr Flow: High-bandwidth thought capture (~100K words/month voice dictation)

Stage 2 - Context Storage:

• Git + Markdown: Deterministic context repository, version-controlled second brain
• Google AI Studio: Free call transcription service

Stage 3 - Context Refinement:

• Claude Code / Gemini CLI: Agentic partners for data synthesis and intelligence generation

Stage 4 - Context Assembly:

• Prompt Tower (VSCode Extension): Ultimate context assembler for massive, holistic prompts
• Gemini: Million-token context window for comprehensive analysis

Professional Output:

• Quarto: Open-source publishing system for dynamic, reproducible documents
• LaTeX / XeLaTeX: Professional typography and document preparation
• RevealJS: Interactive presentation framework

This isn't a random collection of tools—it's a systematic pipeline for manufacturing perfect, context-rich AI collaboration.