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Open Source·MIT

A Methodology for AI-First Development

Structured methodology proven on enterprise mono-repos with millions of lines of code

Start LearningBrowse Prompts
11 Lessons|7 Production Prompts|Research Tools
methodology.md
INVESTIGATE: Trace the code path
ANALYZE: Compare expected vs actual
EXPLAIN: File paths, line numbers, root cause
From the Prompt Library →
"Thank you for not making another video course series"— Reddit

What You'll Learn

11 lessons covering research, planning, execution, and validation patterns

Module 1

Understanding the Tools

  • LLM internals: context, attention, token limits
  • What breaks: hallucinations, code drift, refactoring
  • Context management and RAG integration
Module 2

Methodology

  • Prompt structure: constraints, examples, chain-of-thought
  • Grounding: embedding context that persists
  • Iteration patterns: plan, execute, verify
Module 3

Practical Techniques

  • CI integration and automated review patterns
  • Test generation and coverage strategies
  • Debugging sessions: when AI makes it worse

Learn Your Way

Every lesson, three formats

Reference Docs

Bookmark it. Jump back in when you need it.

Podcasts

Commute, gym, walking the dog.

Presentations

Share with your team.

Open Source Ecosystem

Production-ready tools that apply course methodology

ChunkHound

Don't search your code. Research it.

10K–1M+ LOC
ArguSeek

Wide research, not deep reports

12–100+ sources per query
Curated Toolbox

Modern CLI tools for AI-first development

ripgrep, fzf, lazygit...

The research layer that anchors your agents in reality

ChunkHound and ArguSeek are created by the course author.