collaboration-audit

name	collaboration-audit
description	Use this to map the full human-AI working loop - prompts in, verification out, understanding kept or leaked.

Collaboration Audit

Lens 1: Inputs

Judge prompts by functional properties, never by grammar, brevity, informality, or voice dictation:

• desired outcome present
• relevant context present
• constraints present
• examples present
• success criteria present
• authority boundaries present
• verification requested
• unnecessary instructions
• conflicting instructions

Find the highest-performing prompt patterns in this corpus - the ones associated with fewer correction loops.

Then list instructions the subject retypes manually again and again. Each one is a candidate repo context file, skill, test, or guardrail. Memory is the wrong storage for repeated rules.

Lens 2: Output Handling

After plausible AI output arrives, classify the response: inspect, test, ask why, request sources, run a reproduction, compare alternatives, implement immediately, ship immediately.

Measure verification depth against risk level. Hunt both directions:

• AI SOUNDED CORRECT but result was wrong - what check was skipped
• excessive verification burned hours on a reversible, cheap decision

The goal is calibrated checking, not maximum checking.

Lens 3: Understanding Ledger

Map tasks onto delegation levels: autocomplete, implementer, researcher, debugger, architect, decision adviser, autonomous operator.

Then locate:

• delegation that saved weeks
• delegation that manufactured rework
• where the subject stays intellectually involved and thrives
• where evaluation replaced understanding and cost them later
• concepts repeatedly re-explained in later sessions
• where AI visibly accelerated genuine learning
• where the underlying system became a black box

One-Line Memory

Calibrated checking beats maximum checking.