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Controlled AI Development Process

Micromanaged Driven Development (MMDD) turns AI from an unpredictable code generator into a reliable partner through a systematic 5-step workflow cycle. Instead of hoping for the best with broad requests, MMDD breaks work into small, reviewable units where you and the AI collaborate at every stage.

The 5-Step MMDD Cycle

Section titled “The 5-Step MMDD Cycle”

Create Context

Establish shared understanding before any planning or code. For the project: discuss goals, constraints, and tools. For each unit: verify the AI understands the objective, how it fits the larger picture, and any dependencies.

Key question: “Do we both understand what we’re trying to accomplish and why?”

Plan and Define

With context established, formalize the approach in markdown. The AI drafts the unit file with clear objectives, implementation approach, and success criteria. Review and iterate until the plan is solid before proceeding.

Developer action: Approve the plan or refine it together.

Implementation

Execute the plan in manageable chunks. For code units, decide whether to combine or separate implementation and tests. Break down into logical subunits as needed. Implement incrementally, validating each piece before moving forward.

Best practice: Small steps, constant validation.

Test and Validate

Verify the implementation meets the unit’s objectives. The AI helps execute tests and provides a concise summary of what was implemented. You confirm the unit achieves its stated goals and integrates properly.

Developer action: Validate functionality and integration.

Commit

Finalize with structured git commits. Title format: “Complete Unit XX: [Unit Name]”. Body includes concise description of changes, key files modified, and focuses on “what” and “why” rather than “how”.

Before committing: Update unit markdown status to “Complete” and refresh project status.

Why It Works

Section titled “Why It Works”
  • Predictable AI results – Context and controlled scope reduce hallucinations
  • Maintainable code – Every decision documented in the unit files
  • Faster onboarding – New developers trace the “why” behind changes
  • Confident iteration – Small steps mean safer, reversible decisions
  • Clear collaboration – The 5-step cycle keeps human and AI aligned

MMDD in Action

Section titled “MMDD in Action”

MMDD works! — The methodology itself was developed using MMDD principles. This website was built entirely with MMDD, with every prompt, decision, and iteration documented across 15 units.

Real-world validation: CodeRipple, a complex serverless application for the AWS Lambda Hackathon 2025, was built 100% with AI assistance using MMDD — proving the methodology works for production systems.

Ready to try it? Download the v3.0 principles and start your first unit:

curl -O https://mmdd.dev/00_mmdd.md

See the methodology in action on GitHub and apply it to your next AI coding session.