Pacing Rule — One Section at a Time

Present one section, deliverable, or elicitation move at a time. After presenting — stop. Wait for the client to respond. Do not pre-fill and present multiple sections in one message. Do not move to the next step until the client confirms, corrects, or gives a clear go-ahead.

The goal is a conversation, not a document dump. If you've written more than one section without a client response in between — you've gone too far.

P — Prove: Success Validation

Did the needle move?

Project State

At the start of Phase P: read docs/crisp-state.json. Pull phases.R.baseline and phases.R.successTarget as the measurement baseline. Check phases.S.sprints to know what was built.

At the end of Phase P: update docs/crisp-state.json:

• Add "P" to phases.complete
• Set phases.P.complete to true
• Set phases.P.outcome to "success", "partial", or "fail"
• Add summary notes to phases.P.notes

This phase closes the loop back to Phase R. Three questions drive everything.

1. Did the needle move?

Pull the baseline and targets from docs/success-metrics.md and measure against them:

• Quantitative: Did each metric hit its target? (e.g. "quotes sent in 4min vs 4h before")
• Qualitative: Re-run the survey or interview from the Phase R baseline frustration score — did it improve?
• Second-order effects: Did the downstream impacts materialise? (e.g. cart abandonment dropped after checkout speed improved)

2. Was everything actually built and verified?

Read docs/test-log.md. For every sprint that shipped:

• Is there a test log entry?
• Did every specced behaviour (from the sprint's AI Spec test requirements) get a test written and run?
• Are there any ❌ failures that were never resolved?

A system where the metrics look good but tests weren't written is a system running on luck. The test log is how you know the difference between "it works" and "it worked that one time we checked."

If the test log has gaps — identify what wasn't verified and decide: is this a risk to the outcome measurement, or is it acceptable to close?

3. Did the AI justify itself?

Read docs/problem-statement.md — AI Justification section. The client agreed to custom AI because it could do something off-the-shelf products couldn't.

Ask: did that thing actually materialise?

"We said custom AI was justified because [X]. Did [X] happen? Is the client getting something from this that they couldn't get from Claude.ai at $20/month?"

If yes → close it. If no → document the gap honestly. It's a learning, not a failure — but it should be captured so the next project's justification gate is sharper.

Outcomes

Success → document what worked, close the project, capture learnings.

Partial → identify which metrics hit and which didn't. Was the target unrealistic, or did the solution miss?

Fail → return to Phase R. The problem may have been misdiagnosed, or the success criteria were wrong.

In Serbia, when things go sideways, we make jokes. It's not denial — it's perspective. Balkan humor is the ability to look at a disaster and find the absurdity before you find the culprit. Apply that here. If Phase P says fail, laugh once — then go back to Phase R with fresh eyes. A failed outcome is the most honest piece of data you'll collect in the whole project. The problem was misdiagnosed. Now you know exactly where. That's worth something.

Exit Checklist

• Quantitative metrics measured against baselines in docs/success-metrics.md
• Qualitative scores re-evaluated against Phase R frustration baseline
• Second-order effects checked
• docs/test-log.md reviewed — all sprints have entries, no unresolved ❌ failures
• Every specced behaviour from AI Spec test requirements confirmed as verified
• AI justification validated — did custom AI deliver what justified not using off-the-shelf?
• Success / partial / fail called explicitly — no ambiguity
• Learnings documented (incl. what the test log revealed and whether AI justification held)
• Client signed off