nw-taste-evaluation

name	nw-taste-evaluation
description	Design taste evaluation framework — DVF primary filter, Apple/Google/Jobs design principles as explicit scoring criteria, weighted decision matrix, and option ranking for the DIVERGE wave
user-invocable	false
disable-model-invocation	true

Taste Evaluation

The Taste Problem

Design taste cannot remain tacit. In the age of AI-assisted product development, taste must be encoded as explicit evaluation criteria — operable, auditable, reproducible. Gut feel is the source from which taste criteria are derived; the weighted matrix is the mechanism that makes taste operational.

Key insight: Taste is a fourth lens applied after DVF, not instead of it. An option can be Desirable, Feasible, and Viable — and still fail taste by adding three new concepts to the user's mental model when zero would suffice.

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Phase 1: DVF Filter — Primary Triage

Apply IDEO's three-lens filter first. Any option failing two or more lenses is eliminated before taste scoring.

Lens	Question	Score 1-5
Desirability	Do users want this? Does it address the validated job?	1 = no evidence of want, 5 = clear expressed need
Feasibility	Can we build it with available skills/tools/time?	1 = requires unavailable technology, 5 = straightforward to build
Viability	Does it support a sustainable business model?	1 = no path to revenue/retention, 5 = clear value capture

Elimination threshold: DVF total < 6 → option eliminated before taste scoring.

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Phase 2: Taste Criteria — Four Apple/Jobs Principles

Apply these four criteria to all options that pass DVF. Each is scored 1-5 with explicit rubrics — no subjective override.

Criterion T1: Subtraction

"Innovation is saying no to a thousand things." — Jobs, 1997

Test: Could this option achieve its goal with one fewer feature/concept/step?

Score	Description
5	Nothing can be removed without breaking the core value
4	One minor element could be removed; core intact
3	Multiple removable elements, value unclear without them
2	Clearly bloated; several non-essential parts
1	Feature accumulation masquerading as a product

Criterion T2: Concept Count

"Simplicity is the ultimate sophistication." Cognitive load is a design flaw, not a user problem to solve.

Test: How many new mental concepts does a first-time user need to learn?

Score	Description
5	Zero new concepts — maps entirely to existing mental models
4	One new concept, well-anchored to something familiar
3	Two new concepts, introduced sequentially
2	Three or more concepts, some interdependent
1	Requires a new mental model to operate

Criterion T3: Progressive Disclosure

Complexity must be staged proportionally to user readiness. Front-loading is a design failure.

Test: Does the first interaction expose only what's needed for the first use case?

Score	Description
5	First interaction = one action; depth revealed only on demand
4	First interaction = core flow; secondary features one step removed
3	First interaction exposes 2-3 features; sequencing is logical
2	First interaction requires choosing between multiple paths
1	All capabilities exposed at once; user must learn to ignore

Criterion T4: Speed-as-Trust

Perceived responsiveness is the primary signal users use to assess product quality and reliability. 75% of users who experience slowness do not return (Akamai).

Test: Does this option introduce latency, friction, or steps that erode the sense of speed?

Score	Description
5	Instant feedback; every action has immediate response
4	Minor latency well-masked by progress indicators
3	Noticeable latency but justified by clear payoff
2	Multiple wait points; no perceived control
1	Blocking operations; user cannot tell if it's working

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Phase 3: Weighted Scoring Matrix

Assemble all scores into a weighted matrix.

Default weights (adjust per product type):

Criterion	Default Weight	Developer Tool	Consumer App
DVF (avg)	30%	25%	35%
Subtraction (T1)	20%	15%	25%
Concept Count (T2)	20%	20%	20%
Progressive Disclosure (T3)	15%	15%	10%
Speed-as-Trust (T4)	15%	25%	10%

Final score = Σ(criterion score × weight). Max = 5.0.

Output table:

| Option | DVF | T1 Sub | T2 Concept | T3 Prog | T4 Speed | Weighted Total |
|--------|-----|--------|------------|---------|----------|----------------|
| A      | 4.0 | 5      | 4          | 3       | 4        | 4.05           |
| B      | 3.3 | 3      | 5          | 4       | 5        | 3.84           |
| C      | 4.7 | 2      | 3          | 3       | 2        | 3.28           |

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Phase 4: Recommendation

Produce top 3 options from the scoring matrix.

For each of the top 3, provide:

### Option [Name] — Score [X.XX]

**Why it scores well**: What taste principles it satisfies strongly
**Core trade-off**: What it sacrifices (every option trades something)
**Key risk**: The assumption that must be true for this to work
**Hire criteria**: Under what circumstances would a user choose this?

Recommendation: Identify the top option with a one-paragraph rationale grounded in the scoring — not preference. If the top option has a critical weakness, flag it explicitly.

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Anti-Patterns in Taste Evaluation

Anti-pattern	Detection	Correction
Cherry-picking criteria	Some options evaluated on fewer criteria	Apply all criteria to all options
Retroactive justification	Scores given after recommendation chosen	Score first, recommend after
Weight manipulation	Weights shifted to favor pre-chosen winner	Lock weights before scoring
"It feels right" override	Recommendation contradicts scores	Follow the matrix or change the weights explicitly
Feasibility as tie-breaker only	Low-feasibility options kept for aesthetics	DVF is a filter, not a tiebreaker

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DIVERGE Output for Taste Phase

Produce docs/feature/{feature-id}/diverge/taste-evaluation.md and recommendation.md:

taste-evaluation.md:

1. DVF filter table (eliminations documented)
2. Weights selected and rationale
3. Full scoring matrix for surviving options
4. Score breakdown per criterion per option

recommendation.md:

1. Top 3 options with pro/con/risk/hire-criteria
2. Recommended option with rationale
3. Dissenting case (which option the scoring almost chose instead, and why)
4. Decision for DISCUSS wave: "Proceed with [option], assuming [key risk] is acceptable"

Gate: Recommendation must be derivable from the scoring matrix. Any mismatch between scores and recommendation must be explicitly justified with weight adjustment.