Taste

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name: Taste description: 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:

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:

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

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Load Reference When Needed

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