prompt-engineer-toolkit

Prompt Engineer Toolkit

Overview

Use this skill to move prompts from ad-hoc drafts to production assets with repeatable testing, versioning, and regression safety. It emphasizes measurable quality over intuition. Apply it when launching a new LLM feature that needs reliable outputs, when prompt quality degrades after model or instruction changes, when multiple team members edit prompts and need history/diffs, when you need evidence-based prompt choice for production rollout, or when you want consistent prompt governance across environments.

Core Capabilities

• A/B prompt evaluation against structured test cases
• Quantitative scoring for adherence, relevance, and safety checks
• Prompt version tracking with immutable history and changelog
• Prompt diffs to review behavior-impacting edits
• Reusable prompt templates and selection guidance
• Regression-friendly workflows for model/prompt updates

Key Workflows

1. Run Prompt A/B Test

Prepare JSON test cases and run:

python3 scripts/prompt_tester.py \
  --prompt-a-file prompts/a.txt \
  --prompt-b-file prompts/b.txt \
  --cases-file testcases.json \
  --runner-cmd

Input can also come from stdin/--input JSON payload.

2. Choose Winner With Evidence

The tester scores outputs per case and aggregates:

• expected content coverage
• forbidden content violations
• regex/format compliance
• output length sanity

Use the higher-scoring prompt as candidate baseline, then run regression suite.

3. Version Prompts

4. Regression Loop

1. Store baseline version.
2. Propose prompt edits.
3. Re-run A/B test.
4. Promote only if score and safety constraints improve.

Script Interfaces

• python3 scripts/prompt_tester.py --help
  • Reads prompts/cases from stdin or --input
  • Optional external runner command
  • Emits text or JSON metrics
• python3 scripts/prompt_versioner.py --help
  • Manages prompt history (add, list, diff, changelog)
  • Stores metadata and content snapshots locally

Pitfalls, Best Practices & Review Checklist

Avoid these mistakes:

1. Picking prompts from single-case outputs — use a realistic, edge-case-rich test suite.
2. Changing prompt and model simultaneously — always isolate variables.
3. Missing must_not_contain (forbidden-content) checks in evaluation criteria.
4. Editing prompts without version metadata, author, or change rationale.
5. Skipping semantic diffs before deploying a new prompt version.
6. Optimizing one benchmark while harming edge cases — track the full suite.
7. Model swap without rerunning the baseline A/B suite.

Before promoting any prompt, confirm:

• Task intent is explicit and unambiguous.
• Output schema/format is explicit.
• Safety and exclusion constraints are explicit.
• No contradictory instructions.
• No unnecessary verbosity tokens.
• A/B score improves and violation count stays at zero.

References

• references/prompt-templates.md — 6 production marketing templates (ad copy, email sequence, social repurposing, landing sections, SEO meta, brand-voice rewrite) plus generic building blocks; each written to be graded by prompt_tester.py
• references/technique-guide.md — technique-selection table for marketing tasks + the LLM-governance stack for marketing teams (claim discipline, disclosure rules, data boundaries, human-review gates)
• references/evaluation-rubric.md — mechanical scoring weights, acceptance gates, marketing quality dimensions, test-suite design, and eval anti-patterns
• README.md

Evaluation Design

Each test case should define:

• input: realistic production-like input
• expected_contains: required markers/content
• forbidden_contains: disallowed phrases or unsafe content
• expected_regex: required structural patterns

This enables deterministic grading across prompt variants.

Versioning Policy

• Use semantic prompt identifiers per feature (support_classifier, ad_copy_shortform).
• Record author + change note for every revision.
• Never overwrite historical versions.
• Diff before promoting a new prompt to production.

Rollout Strategy

1. Create baseline prompt version.
2. Propose candidate prompt.
3. Run A/B suite against same cases.
4. Promote only if winner improves average and keeps violation count at zero.
5. Track post-release feedback and feed new failure cases back into test suite.