arch-mvp-roadmap

name	arch-mvp-roadmap
description	MVP definition, MoSCoW prioritization, and phased delivery planning. Use for scoping minimum viable products, ordering features by value and dependency, or creating implementation roadmaps. Use when this capability is needed.
metadata	{"author":"ai-enhanced-engineer"}

MVP & Roadmap Planning

Patterns for defining what to build first and in what order.

Core Principle

"Build the smallest thing that proves value."

An MVP is not a half-built product—it's a complete vertical slice that validates assumptions.

MoSCoW Prioritization

Priority	Definition	Criteria
Must Have	System doesn't function without	Core journey incomplete, no workarounds
Should Have	Important but not blocking	Workarounds exist, high value
Could Have	Nice to have	Enhances experience, low priority
Won't Have	Explicitly out of scope	Prevents scope creep, document for later

MVP Scoping Checklist

• Single complete user journey end-to-end
• Validates core assumption/hypothesis
• Deployable and demonstrable
• Measurable success criteria defined
• No features without corresponding tests

Phased Delivery Pattern

Each phase should:

1. Build on previous phase (not parallel development)
2. Be independently deployable
3. Have clear success criteria
4. Include tests for new functionality

Dependency Ordering

Order features by:

Factor	Question
Technical	What must exist first?
Value	What provides most value soonest?
Risk	What validates riskiest assumptions?
Learning	What teaches us most about the domain?

Roadmap Template

Phase	Features	Success Criteria	Dependencies
MVP	[Must-haves]	[Measurable outcomes]	None
Phase 2	[Should-haves]	[Measurable outcomes]	MVP complete
Phase 3	[Could-haves]	[Measurable outcomes]	Phase 2 complete

Extensible Algorithm Design

Design algorithms for evolution using stable interfaces. MVP delivers value immediately while building ground truth for future ML phases.

Evolution path:

1. MVP: Deterministic algorithm (keyword matching, rule-based)
2. Phase 2: Statistical approach (TF-IDF, collaborative filtering)
3. Phase 3: ML/embeddings (neural networks, transformers)
4. Phase 4: LLM-powered (if needed)

Interface pattern:

class Categorizer(Protocol):
    def categorize(self, text: str) -> tuple[str, float]:
        """Returns (category, confidence_score)"""
        ...

Key principles:

• MVP uses zero ML infrastructure (no model serving, no embeddings DB)
• Interface stays stable across phases (same input/output signature)
• Each phase is independently measurable (track accuracy improvement)
• Switch implementations via dependency injection, not rewrite

See reference.md for detailed patterns and examples.md for sample roadmaps.

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