nWave-ai 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.
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.
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 |
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 |
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.
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 |
DIVERGE Output for Taste Phase
Produce docs/feature/{feature-id}/diverge/taste-evaluation.md and recommendation.md:
taste-evaluation.md:
- DVF filter table (eliminations documented)
- Weights selected and rationale
- Full scoring matrix for surviving options
- Score breakdown per criterion per option
recommendation.md:
- Top 3 options with pro/con/risk/hire-criteria
- Recommended option with rationale
- Dissenting case (which option the scoring almost chose instead, and why)
- 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.
Source: nWave-ai/nWave → nWave/skills/nw-taste-evaluation/SKILL.md
Also appears in: nWave-ai/nWave/plugins/nw/skills/nw-taste-evaluation/SKILL.md