Prompt Engineering

When to use

• Design and evaluate prompts or LLM workflows on representative tasks.

Not for: skill-authoring for skills; research for model comparisons.

Steps

1. Define inputs, expected outcomes, trust boundaries, failure costs and representative cases.

2. Establish the existing baseline; add minimal instructions addressing observed gaps and verify current provider behavior.

3. Specify output validation and actual tool permissions, budgets and termination; prompt text alone is not a security boundary.

4. Run representative and adversarial cases; compare quality, latency and cost and report limitations.

Output: Suggested paths, subject to human audit: docs/design/PROMPT.md for a maintained workflow.

Verify

• Revisions have a stated baseline and rubric; invalid output and untrusted input are handled and unrun cases are explicit.

References

• engineering-principles.md — shared decision boundaries, scope and verification.
• human-audit.md — document consent, readability and human review.
• references/prompt-architecture.md — prompt component breakdown, few-shot patterns, structured-output techniques, model selection matrix, eval-harness setup, guardrail catalog