prompt-pattern-selection

Skill: Prompting-pattern selection

Pick the cheapest pattern that clears the reliability bar. Every step up the ladder buys reliability with tokens and/or latency — so start low and climb only on evidence of failure.

Step 0 — One opinion up front

Default to zero-shot. A clear, well-ordered instruction solves more than people expect. Reach for examples, CoT, or chaining only when a zero-shot prompt measurably fails the hard cases — not preemptively.

Step 1 — Characterize the failure

Run the prompt on the hard/edge cases and name how it fails:

• Format/style drift the model can't infer from words → few-shot.
• Wrong multi-step reasoning → CoT (one chain) or decomposition (separate jobs).
• Wrong persona/domain register → role framing.
• Occasional wrong high-stakes answer → self-consistency (sample N, vote).
• Doing several jobs at once → decomposition, always.

Step 2 — Trace the tree

Traverse ../../knowledge/prompt-decision-trees.md §1 to a leaf. Record the path and the runner-up.

Step 3 — Price the choice

Name what the pattern costs: few-shot adds input tokens; CoT adds output tokens + latency; decomposition adds calls (latency, orchestration); self-consistency multiplies cost by N. If the cost isn't justified by the reliability gain, drop back.

Step 4 — Verify empirically

Run the chosen pattern against the regression set (owned by prompt-reliability-engineer). Keep it only if it beats the simpler pattern on evidence, not on a single lucky example.

Step 5 — Hand off

• The wording + example curation → prompt-implementation-engineer.
• The eval + regression gate → prompt-reliability-engineer.
• If wording can't fix it, the problem is architectural → prompt-architect.

Output

A pattern recommendation with the tree path, the runner-up and why it lost, the token/latency cost named, and the empirical check that justified it.