| name | kai-taste |
| description | Audit or design generative AI interfaces against three diagnostic pillars (deterministic-stochastic balance, interaction density, visual cohesion). Treats taste as a measurable control system, not subjective preference. Use when: 'taste audit', 'score this UI', 'design quality', 'interaction density', 'visual cohesion', 'refiner layer', 'correction cost', 'why does this feel off', 'polish this', 'design review', or building any user-facing AI product. |
/kai-taste — a scored diagnosis of why an AI product feels off, and what to change
Objective
Taste treated as a control system: the thing that converts stochastic model output into reliable user outcomes at minimal correction cost. Two modes share one destination — a defensible position in the three-pillar space with evidence behind every claim.
Audit mode produces a scored diagnosis of an existing UI, product, or generated output: a number per pillar, a present/absent call on all eight failure modes with the observation that supports it, whichever north-star metrics are computable, and a prioritized fix list.
Design mode produces a taste contract for something being built: which zones are deterministic, which are stochastic, what must never happen, and the instrumentation that will show whether the design landed.
Iron law: taste stays subordinate to function. The moment the system's correction vector dominates the user's intent vector, the product crosses from high-fidelity to high-friction.
Done when
Work type audit-report — floor E3/C4/O1 (harness/eco-floors.yaml). Design-mode output ships inside the feature it informs, and that feature's own work type governs its floor.
- E3 — a named human approved the exact scorecard, and every score, failure-mode call, and metric resolves to a recorded observation: a screenshot at a stated viewport, a snapshot, a source file and line, or a session recording.
- C4 — provenance holds.
banned_word_check clean, and no score, metric, or claim is estimated. A pillar that cannot be observed is scored as unobserved with the missing evidence named — never averaged in from impression.
- O1 — every P0 fix names the metric it targets (correction density, time-to-value, dismissal rate, clarification burden), its current value or the reason it cannot be read yet, the threshold that counts as fixed, and an owner.
Grade bands on the composite (three pillars, /30): 25–30 = A, taste is a competitive asset · 20–24 = B, solid with minor gaps · 15–19 = C, functional but friction-heavy · 10–14 = D, taste is actively hurting the product · below 10 = F, taste is absent or harmful.
Constraints
- Score all three pillars and scan all eight failure modes every audit. A partial scan reports a partial product.
- Read
references/pillar-rubrics.md before assigning a number. The 1–10 criteria carry observable evidence requirements; a score without the rubric is a preference.
- Fixes are ranked P0 (blocking UX) / P1 (significant friction) / P2 (polish). P0s clear before ship.
- A taste contract states testable constraints, never adjectives. Reading level, verbosity ceiling, required sections, prohibited moves, citation rules, formatting grammar, interaction rules — each one checkable.
- Pair every metric with a counter-metric. Proxies improve while quality degrades whenever the proxy becomes the target (Goodhart), so periodic qualitative audits sit alongside the numbers.
- Moderate kinetic friction is the target. Too low produces oracle trust, where users accept output without thinking. Too high means navigation overhead eats the productivity gain.
- Polish that outpaces reliability is a defect. Never smooth a caveat, never use persona polish to mask uncertainty, and never let surface coherence stand in for correctness.
- Live-URL audits capture all three viewports — 375px mobile, 768px tablet, 1440px desktop — before any score is assigned.
Context
| Need | Load / run |
|---|
| 1–10 scoring criteria with observable evidence | references/pillar-rubrics.md |
| The 10-step Refiner Layer (design mode) | references/refiner-protocol.md |
| Information theory, neuroscience, cognitive load — the "why" | references/theory-foundations.md |
| Auditing a live URL | Browse daemon: $B goto <url> · $B snapshot -i -a · $B screenshot |
| Auditing code or mockups | Read the source and rendered output directly, score against the rubrics |
| The full theoretical foundation, when a score needs defending | docs/research/taste/taste.md (taxonomy, North Star, 5-step Refiner) · docs/research/taste/chatgpt_taste.md (engineering framework, proxy metrics) · docs/research/taste/deep-research-report.md (neuroscience, cognitive load, agency) · docs/research/taste/AI Design Taste_ A Systems Approach.md (information theory, style weights) · docs/research/taste/Engineering AI Design Taste Framework.md (operationalization, failure modes) |
The three pillars. Every generative AI product sits somewhere in this space; the pillars form a feedback system, so moving one shifts the feasible region of the others.
| Pillar | Measures | Control knobs | Bad signal |
|---|
| Deterministic-Stochastic Balance | Where entropy enters the pipeline — where the system is creative vs reproducible | Entropy budgeting per phase, multi-sample + rerank, structured outputs, tool calls as determinism anchors | "Why did it change?" — users fight the AI's personality instead of steering it |
| Interaction Density | Affordances per unit of cognitive load — the cost-per-outcome ratio | Progressive disclosure, correction as first-class UI, chat-to-canvas for persistent artifacts | The user manages the interface more than the task |
| Visual Cohesion | Perceptual grammar consistency across outputs and states | Design tokens + component grammar, semantic structure before styling, affordance protection | Output looks dropped in from another system |
Failure modes. Too much taste is a system failure. Scan for all eight:
| Mode | Detection signal | Antidote |
|---|
| Stochastic Over-Constraint | Revision entropy rises; users fight the model's personality | Recalibrate entropy injection points; let the user steer creative vs deterministic |
| Density Paralysis | Choice overload; mental model fragments (Hick's Law) | Progressive disclosure; layer density spatially |
| Cohesion Rigidity | Output locked in a narrow aesthetic band; system fights divergent intent | Visual escape hatches; parameterize cohesion |
| Oracle Polish | Surface coherence bleeds into perceived correctness (halo effect) | Confidence-aware UI; expose uncertainty; never smooth caveats |
| Affordance Collapse | Users miss actions; content and controls are indistinguishable | Buttons must look like buttons; test discoverability with real users |
| Interaction Ceremony | Time-to-value inflated by wizards, confirmations, tone selectors | Remove any step that does not reduce correction cost; measure TTV |
| Trust Distortion | Users accept output because it sounds right, unverified | Calibrate confidence to actual reliability |
| Metric Gaming | Proxies improve while product quality degrades (Goodhart) | Counter-metric per metric; periodic qualitative audits |
North star metrics. Taste is a latent variable; these are its proxies.
| Metric | Formula | Bad looks like |
|---|
| Refinement Velocity | Vr = 1 / n_prompts | 5+ turns to something usable |
| Correction Density | Dc = manual_edits / generated_tokens | Heavy cleanup on every output |
| Kinetic Friction | Fk = t_action - t_render | Too fast (oracle trust) or too slow (navigation overhead) |
| Time-to-Value | Seconds to a first artifact surviving 30s unmodified | Slow first output; first-session churn |
| Correction Effort | Edit distance from generated to accepted | Heavy reformatting before it is usable |
| Dismissal Rate | % of suggestions ignored, collapsed, or dismissed | Users suppress the system to get work done |
| Clarification Burden | Turns before a stable artifact exists | Users explain instead of building |
Design-mode gates, run before shipping a user-facing AI feature. Balance: entropy injection points identified, structured outputs enforced for downstream actions, tool calls for factual queries, multi-sample + rerank where quality matters, user-togglable exploration vs execution. Density: shallow default path, cheap correction (edit-in-place, scoped refine, accept/reject diffs), contextual advanced controls, persistent artifacts for multi-step work, state externalized as manipulable objects. Cohesion: outputs compile through component grammar, semantic structure before styling, affordance protection, tokens consistent across states, generated content indistinguishable from hand-authored. Anti-failure: uncertainty signaled rather than smoothed, user can override or escape suggestions, no step that fails to reduce correction cost, trust calibrated to reliability, selective friction preserved to protect agency.
Corrections to common assumptions, worth stating because each one reverses a default instinct:
| Assumption | Reality |
|---|
| Taste is subjective and cannot be scored | It has measurable proxies: correction cost, time-to-value, dismissal rate |
| More polish means more taste | Oracle Polish is a failure mode; polish outpacing reliability destroys trust |
| Minimize all friction | Selective friction builds ownership (IKEA effect); remove it only where it does not reduce understanding |
| The model output is the product | The model is a probabilistic component; the Refiner Layer makes it feel authored |
| Chat is good enough | Chat serializes multi-dimensional state into one stream; canvas externalizes memory |
| Make it beautiful and it is tasteful | Cohesion Rigidity and Affordance Collapse are both beauty-caused |
| Users want everything instantly | Optimal pacing is layered: acknowledgment 0.1s, provisional structure 1s, deferred commitment seconds |
| Show confidence to build trust | Uncalibrated confidence creates the uncanny valley of agency; transparency builds trust |
Escalate when
- A pillar cannot be observed with the access available — say so and score it unobserved rather than inferring.
- Metrics require instrumentation the product does not have, and the fix list depends on them.
- A P0 fix conflicts with a stated product or business constraint.
- The requested polish would raise perceived confidence above measured reliability.
- Audit findings imply changes to a live product surface that nobody has authorized.
