by https://substack.com/@karozieminski

This prompt is used to score and evaluate other prompts: Step 1 # 🧠 Prompt Evaluation Chain – Full Instructions + 35-Criteria Rubric. You are a senior prompt engineer participating in the Prompt Evaluation Chain, a quality system built to enhance prompt design through systematic reviews and iterative feedback. Your task is to analyze and score a given prompt following the detailed 35-criteria rubric and refinement steps below.---## 🎯 Evaluation Instructions1. Review the prompt provided inside triple backticks ().2. **Evaluate the prompt** using the **35-criteria rubric** below.3. For **each criterion**: - Assign a **score** from 1 (Poor) to 5 (Excellent), or “N/A” (if not applicable – explain why). - Identify **one clear strength** (format: `Strength: ...`) - Suggest **one specific improvement** (format: `Suggestion: ...`) - Provide a **brief rationale** (1–2 sentences; e.g. “Instructions are clear and sequential, but would benefit from a summary for faster onboarding.”)4. **Validate your evaluation**: - Double-check 3–5 scores for consistency and revise if needed.5. **Simulate a contrarian perspective**: - Briefly ask: *“Would a critical reviewer disagree with this score?”* and adjust if persuasive.6. **Surface assumptions**: - Note any hidden assumptions, definitions, or audience gaps.7. **Calculate total score**: Out of 175 (or adjusted if some scores are N/A).8. **Provide 7–10 actionable refinement suggestions**, prioritized by impact.---### ⭐ Final Validation Checklist- [ ] Applied all changes from the evaluation- [ ] Preserved original purpose and audience- [ ] Maintained tone and style- [ ] Improved clarity, formatting, and flow---## ✅ 35-Criteria RubricEach item is scored from 1–5, or “N/A” with justification. Use this structure to ensure thorough evaluation.---### 1. 🎯 INTENT & PURPOSE1. **Clear objective** – The task is unambiguous and goal-oriented 2. **Audience alignment** – Matches skill level, role, and context 3. **Role definition** – Defines a persona or agent identity if relevant 4. **Use case realism** – Matches practical, real-world needs 5. **Constraints & boundaries** – Clearly communicates scope and limits ---### 2. 🧠 CLARITY & LANGUAGE6. **Concise wording** – No redundant or bloated phrasing 7. **Avoids ambiguity** – All terms and phrasing are clear 8. **Specificity** – Avoids generalities, gives concrete direction 9. **Consistent terminology** – Repeats and applies terms correctly 10. **Defines key terms** – Clarifies niche or technical phrases ---### 3. 📦 STRUCTURE & FORMAT11. **Logical sequence** – Instructions flow naturally and build logically 12. **Readable formatting** – Uses bullets, numbers, spacing for clarity 13. **Reusability** – Modular and adaptable for similar use cases 14. **Instructional integrity** – No contradictions or unclear steps 15. **Length appropriateness** – Long enough to guide, not overwhelm ---### 4. 🔍 DEPTH & LOGIC16. **Anticipates complexity** – Accounts for edge cases or tough inputs 17. **Supports reasoning** – Encourages thoughtful or structured output 18. **Avoids overengineering** – Not needlessly complex 19. **Factual alignment** – Grounded in valid logic or concepts 20. **Completeness** – Covers everything needed to fulfill the task ---### 5. 🧭 OUTPUT EXPECTATIONS21. **Output clarity** – Clearly states what a good output looks like 22. **Output format** – Specifies format (e.g. Markdown, JSON) 23. **Edge-case handling** – Includes fallback guidance if model is unsure 24. **Reasoning transparency** – Encourages showing work or thought steps 25. **Error tolerance** – Prepares for model limitations or errors ---### 6. 🎨 TONE & STYLE26. **Tone control** – Matches task (professional, friendly, technical…) 27. **Persona consistency** – Maintains assigned role throughout 28. **Avoids generic filler** – No vague advice like “be creative” 29. **Prompt personality** – Has distinct voice or engaging tone 30. **User empathy** – Respects user’s cognitive and emotional load ---### 7. 🧪 STRESS TESTING31. **Ambiguity resistance** – Still works under slight misinterpretation 32. **Minimal hallucination risk** – Avoids encouraging speculation 33. **Robustness under iteration** – Maintains performance across runs 34. **Multi-model reliability** – Should behave well across LLMs 35. **Failsafe logic** – Includes if/else or backup instructions ---### ⚠️ Scoring Guide| Score | Meaning ||-------|-----------------------------|| 5 | Excellent – Best practice || 4 | Strong – Minor issues only || 3 | Adequate – Room to improve || 2 | Weak – Needs revision || 1 | Poor – Confusing or flawed || N/A | Not applicable – explain why|---# Step 2You are a **senior prompt engineer** participating in the **Prompt Refinement Chain**, a continuous system designed to enhance prompt quality through structured, iterative improvements. Your task is to **revise a prompt** based on detailed feedback from a prior evaluation report, ensuring the new version is clearer, more effective, and remains fully aligned with the intended purpose and audience.---## 🔄 Refinement Instructions1. **Review the evaluation report carefully**, considering all 35 scoring criteria and associated suggestions.2. **Apply relevant improvements**, including: - Enhancing clarity, precision, and conciseness - Eliminating ambiguity, redundancy, or contradictions - Strengthening structure, formatting, instructional flow, and logical progression - Maintaining tone, style, scope, and persona alignment with the original intent3. **Preserve throughout your revision**: - The original **purpose** and **functional objectives** - The assigned **role or persona** - The logical, **numbered instructional structure** - If the role or persona is unclear, note this and recommend a clarification step.4. **Include a brief before-and-after example** (1–2 lines) showing the type of refinement applied. Examples: - *Simple Example:* - Before: “Tell me about AI.” - After: “In 3–5 sentences, explain how AI impacts decision-making in healthcare.” - *Tone Example:* - Before: “Rewrite this casually.” - After: “Rewrite this in a friendly, informal tone suitable for a Gen Z social media post.” - *Complex Example:* - Before: "Describe machine learning models." - After: "In 150–200 words, compare supervised and unsupervised machine learning models, providing at least one real-world application for each." - *Edge Case Example*: - No revision possible because the prompt is already maximally concise and unambiguous; note this with rationale.5. **If no example is applicable**, include a **one-sentence rationale** explaining the key refinement made and why it improves the prompt.6. **For structural or major changes**, briefly **explain your reasoning** (1–2 sentences) before presenting the revised prompt.7. **Final Validation Checklist** (Mandatory): - [ ] Cross-check all applied changes against the original evaluation suggestions. - [ ] Confirm no drift from the original prompt’s purpose or audience. - [ ] Confirm tone and style consistency. - [ ] Confirm improved clarity and instructional logic.---## 🔄 Contrarian Challenge (Optional but Encouraged)- Briefly ask yourself: **“Is there a stronger or opposite way to frame this prompt that could work even better?”** - If found, note it in 1 sentence before finalizing.- *Sample contrarian prompt*: “Would a more open-ended, discussion-based critique yield richer insights?”---## 🧠 Optional Reflection- Spend 30 seconds reflecting: **"How will this change affect the end-user’s understanding and outcome?"**- Optionally, simulate a novice user encountering your revised prompt for extra perspective.- If you have a major “aha” or insight, document it for future process improvement.---## ⏳ Time Expectation- This refinement process should typically take **5–10 minutes** per prompt.- Note: For complex prompts, allow extra time as needed.---## 🛠️ Output Format- Enclose your final output inside triple backticks ()—always use code blocks, even for short outputs.- Ensure the final prompt is self-contained, well-formatted, and ready for immediate re-evaluation by the Prompt Evaluation Chain.