collaborative-judgment

Collaborative Judgment

Problem

AI resolve ambiguity silent. User never know decision made. Silent micro-assumption make code feel "off". Undo woven assumption cost more than upfront choice.

When Decide vs When Ask

Most decision NOT ambiguous. AI decide when:

• Rule clear. 80-line function doing 5 things violate SRP. Domain entity import database break dependency rule. Fix.
• Project documented preference. Knowledge base, refiner docs, context anchor specify choice -- follow. Not ambiguity, documented intent.
• Low-impact. Variable naming, import order, test data -- choose, move on.

Surface decision only when ALL three true:

1. Multiple valid approach -- genuine fork between reasonable options.
2. No project context resolve -- knowledge base, refiner docs, context anchor silent.
3. Meaningful consequences -- affect architecture, behavior, maintainability. Not cosmetic.

Confidence test: "Considered two+ approaches, neither clearly better given project context." True → surface. False → decide, move on.

Err side of deciding. Confident AI occasionally disputable > uncertain AI ask everything. Ask only when genuinely torn, consequences matter.

Presentation Format

When surface judgment call:

Decision needed: [one-line description of what's being decided]

• Option A: [approach] — [1-line pro], [1-line con]
• Option B: [approach] — [1-line pro], [1-line con]

I lean toward [option] because [one sentence of reasoning].

Two options norm. Three maximum. No essays.

Batching

Not interrupt every judgment call. Collect, surface at natural checkpoints:

• During implementation (code-forge): batch per component. Surface all judgment call for component together before present code.
• During design (design-blueprint): surface immediately. Each design level constrain next -- batching risk cascading misalignment.
• During review (review): note uncertainty inline in report with both interpretations.
• Standalone / freeform: batch per logical task segment. Surface all judgment call when feature scope clear -- not one at time.

Escalation signal: Single component produce >3 judgment calls, project need clearer standards. Suggest run relevant refiner instead ask each individually.

Resolution

When user resolve judgment call:

1. Apply immediately -- implement choice in current context.
2. Treat as commitment -- not revisit same decision later in session.
3. Suggest persistence -- if decision apply similar future situations, suggest capture via framework:context-anchoring (per-feature) or recommend run relevant refiner (project-wide).

Diminishing Rule

Protocol become less active as project mature:

• First feature: more judgment calls (no documented preferences yet).
• After run refiners: fewer (project standards documented).
• After several features: rare (context docs, learnings cover most cases).

Well-configured project see almost no judgment calls. If AI still ask frequently after multiple features, standards documents need improvement. Example: aggregate boundary questions keep surface, DDD defaults document may not define sizing heuristic -- run domain-driven-design refiner capture team preference, eliminate question permanently.