product-principles

Product Management Principles

Core Philosophy

1. Hypothesis Until Proven: Every assumption is a hypothesis until validated with evidence. Treat unvalidated ideas as hypotheses, not facts
2. Value Traceability: From hypothesis to validation to user story to PRD to implementation to test — maintain traceability across the entire chain
3. Feedback Accumulation: Every outcome (including failures) is a learning asset. Never delete invalidated hypotheses — they inform future decisions
4. Validated Enough, Not Perfect: Don't wait for perfect validation. Use cost x risk x reversibility to determine sufficient confidence
5. Artifacts Over Notes: Intermediate analysis (market research, competitive landscape, brainstorming) is conducted within the conversation and synthesized into the designated artifact. Each recipe defines its output locations

Opportunity Solution Tree (OST) Hierarchy

All product work follows this hierarchy:

Outcome
  ├── Product Outcome (team-controllable product goals)
  │     NSM connects Product Outcome ↔ Business Outcome
  └── Business Outcome (business results Product Outcome contributes to)

Product Outcome
  └── Opportunity (user problems, needs, desires)
        └── Solution (approaches to address the opportunity = feature candidates)
              └── Assumption (premises underlying the solution = hypotheses)
                    └── Experiment (methods to validate the hypothesis)

Level Definitions

Level	Granularity	Artifact	Description
Business Outcome	Largest	docs/product/vision.md	Business results the product contributes to
Product Outcome	Large	docs/product/vision.md	Team-controllable product goals
Opportunity	Large	docs/discovery/opportunities/	User problems, needs, desires
Solution	Medium	PRD (docs/prd/)	Feature candidates addressing an Opportunity
Assumption	Small	docs/discovery/hypotheses/	Premises underlying a Solution
User Story	Smallest	Within PRD	Minimum unit of value with all 4 Risks validated

4 Risks (Authoritative Definition)

A user story is the minimum unit of value. All four risks must be sufficiently validated:

• Value — Will users use/buy this? Does it solve their problem?
• Usability — Can users figure out how to use it? Does the UX work?
• Feasibility — Can we build it technically? Is the effort realistic?
• Viability — Does it work as a business? Can we explain why we're building it?

Confidence Meter (Authoritative Definition)

Track confidence per risk dimension (0-10):

Score	Meaning	Typical Evidence
0-2	Gut feeling / no evidence	Assumption only
3-4	Structured evaluation	Expert review, competitive analysis, scoring
5-7	Data-backed	Analytics, surveys, interview patterns
8-10	Tested and confirmed	Prototype validation, A/B test, beta results

Threshold by Cost x Risk x Reversibility

Condition	Confidence Needed	Evidence Level
Low-cost, reversible (feature flag, gradual rollout)	3-4	Structured evaluation
Medium cost	5-7	Data
High-cost, irreversible (platform change, pricing change)	8+	Test results

PRDs must show each user story's current confidence and remaining risks. Enable PO/DRI to judge "validated enough for delivery", not just "fully validated".

Knowledge Pyramid (Authoritative Definition)

Knowledge is organized in three tiers to manage context as hypotheses accumulate:

Tier	Scope	Location	Loading
Tier 1	Distilled product principles	docs/product/learnings.md	Always (via this skill)
Tier 2	Opportunity-level learnings	Each Opportunity file's "Tier 2 Learnings" section	When working on that Opportunity
Tier 3	Individual hypothesis files	docs/discovery/hypotheses/	On demand

Tier 1 learnings are validated patterns derived from 3+ independent hypotheses. Treat them as established principles until re-validated.

Distillation criteria (enforced by knowledge-distiller):

• 3+ Rule: 3+ independent hypotheses required for Tier 1 promotion
• Cross-segment consistency: Must hold across 2+ user segments/contexts
• Contradiction handling: Conflicting evidence recorded with conditions, never discarded
• Freshness tags: All Tier 1 learnings get last-validated dates; 6-12 months without re-validation triggers review

State Design (Authoritative Definition)

Every user-facing interaction must account for these states:

State	Description
Loading	Data is being fetched/processed — show progress indicator
Empty	No data exists yet — guide user to first action
Error	Something went wrong — explain what happened, offer recovery
Partial	Some data available, some not — show available, indicate missing
Success	Normal state with data — primary design focus

PRDs should specify behavior for all states in acceptance criteria. Prototypes should demonstrate at minimum: empty, success, and error states.

Key Principles for Daily Decisions

• 3+ Solutions Test: If an Opportunity can't generate 3+ different Solutions, it may actually be a Solution disguised as an Opportunity (Torres principle). See references/opportunity-template.md for Opportunity file structure
• Don't Kill the Product: Never sever the connection to business outcomes, but use NSM to balance against pure metric-chasing pressure
• Design is a Perspective, Not a Phase: Design thinking applies across all processes — discovery, validation, definition, delivery, and reflection
• Cycle, Not Phases: Discovery → Validation → Definition → Delivery → Reflection is a continuous cycle. Start from anywhere
• MVP Scoping: When transitioning validated hypotheses to PRD, use references/mvp-definition.md for prioritization (MoSCoW/RICE) and scope reduction techniques

Common Pitfalls (Why These Principles Exist)

These principles exist to counter natural tendencies in product thinking:

• 3+ Solutions Test counters the tendency to treat the first Solution idea as the Opportunity itself. When only one Solution comes to mind, the framing is likely too narrow
• Confidence Meter (0-10) counters all-or-nothing thinking about validation. The threshold varies by cost x risk x reversibility because not everything needs the same evidence level
• Knowledge Pyramid tiers counter both context overload (loading every hypothesis) and knowledge loss (forgetting past learnings). The 3+ rule for Tier 1 promotion ensures principles are grounded, not anecdotal
• State Design counters the tendency to design only for the happy path. Acceptance criteria that cover only Success state miss the states users encounter most during onboarding