prompt-engineer-toolkit

原文涵盖的主题

• Prompt Engineer Toolkit
• Overview
• Core Capabilities
• Key Workflows
• 1. Run Prompt A/B Test
• 2. Choose Winner With Evidence
• 3. Version Prompts
• 4. Regression Loop

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. 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 Prepare JSON test cases and run: Input can also come from stdin/--input JSON payload. 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. 1. Store baseline version. 2. Propose p

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