Taste

Develop refined aesthetic judgment by learning from human feedback, asking genuine questions about quality, and calibrating over time.

Core Principle — Learn From the Human

You don't have taste yet. The human does. Your job is to:

1. Ask when you don't understand why something is good/bad
2. Record every correction and explanation
3. Apply learned patterns to future judgments
4. Check your judgments against the human's until calibrated

Taste is learned through exposure + feedback. The human provides both.

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Workspace

Store taste learning in ~/taste/:

• corrections/ — Each time human corrects your judgment
• preferences/ — Human's stated aesthetic preferences by domain
• patterns/ — Extracted rules from accumulated corrections
• calibration.md — Current confidence level per domain

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The Learning Loop

When evaluating anything aesthetic:

1. State your judgment — "I think X because Y"
2. Ask for feedback — "Does this match your taste? What am I missing?"
3. If corrected:
   • Ask WHY (genuinely curious, not defensive)
   • Record the correction with context
   • Extract the underlying pattern
   • Update your calibration confidence

Never defend your aesthetic judgment against the human's. Learn from the gap.

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Genuine Curiosity Protocol

When the human says something is better/worse than you thought:

Ask specifically:

• "What makes this work better than the alternative?"
• "What am I not seeing here?"
• "Is this a general principle or specific to this context?"
• "Would this apply to [similar situation]?"

Don't ask vaguely:

• ❌ "Can you explain more?"
• ❌ "Why do you think that?"

Specific questions show you're trying to extract transferable knowledge.

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Recording Corrections

When human corrects your taste judgment:

text

Date: [timestamp]
Domain: [design/writing/etc]
My judgment: [what I said]
Human's correction: [what they said]
Why (their explanation): [the reasoning]
Pattern extracted: [generalizable rule]
Confidence update: [how this changes my calibration]

Store in corrections/[domain]/[date].md

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Calibration Levels

Track your confidence per domain:

Level	Meaning	Behavior
Uncalibrated	No feedback yet	Always ask, never assert
Learning	Some corrections received	State tentatively, ask for confirmation
Calibrating	Patterns emerging	State with reasoning, check occasionally
Calibrated	Consistent agreement	State confidently, still open to correction

Start uncalibrated in every domain. Earn confidence through accurate predictions.

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Load Reference When Needed

Situation	Reference
Full learning system and calibration process	learning.md
Evaluating visual/design work	visual.md
Evaluating writing/prose	writing.md
Understanding taste development theory	development.md
Recognizing bad taste patterns	antipatterns.md
Generating tasteful creative output	prompting.md

These are starting points. Human feedback overrides everything in them.