Prompt Evaluation Harness Builder for Reusable AI Prompts

You are an expert prompt engineer and AI quality evaluator. Your task is to design a comprehensive evaluation harness for the prompt provided below.

Context:
- Prompt to evaluate: [Prompt to evaluate]
- Prompt purpose: [Prompt purpose]
- Target audience: [Target audience]
- Intended AI model or tool: [Intended AI model or tool]
- Expected output format: [Expected output format]
- Brand voice or tone requirements: [Brand voice or tone requirements]
- Safety, compliance, or policy constraints: [Safety, compliance, or policy constraints]
- Known weaknesses or concerns: [Known weaknesses or concerns]
- Success criteria: [Success criteria]
- Definition of done: [Definition of done]

Your task is to create a reusable prompt evaluation harness that can be used to test, score, and improve this prompt over time.

Analyze the prompt for:

Clarity and completeness
Instruction-following
Output consistency
Accuracy and factual reliability
Tone and audience fit
Safety and compliance risks
Edge-case handling
Resistance to ambiguous, incomplete, or conflicting inputs
Reusability across different scenarios
Production readiness

Create a structured evaluation harness with the following sections:

1. test_cases

 Create structured test cases covering:
- Typical use cases
- Edge cases
- Ambiguous inputs
- Incomplete inputs
- Conflicting instructions
- Unsafe or policy-sensitive requests
- Low-quality user inputs
- High-stakes scenarios, if applicable

For each test case, include:

- id
- scenario
- input
- expected_output
- evaluation_focus
- likely_failure_modes
- pass_criteria

2. scoring_rubric

Create a quantitative and qualitative scoring rubric from 1 to 5 for each major evaluation dimension:
- clarity
- completeness
- accuracy
- instruction_following
- output_format_consistency
- tone_fit
- safety
- edge_case_handling
- practical_usefulness
- production_readiness

For each score level, explain what a poor, acceptable, good, and excellent result looks like.

3. regression_checks
- Define checks that should be repeated whenever the prompt is updated. Include:
- baseline test cases that must always pass
- output format checks
- safety checks
- tone checks
- consistency checks
- failure-mode checks
- automation-friendly checks, where possible

4. improvement_recommendations

Provide prioritized recommendations for improving the prompt. For each recommendation, include:
- priority: high, medium, or low
- issue
- why_it_matters
- suggested_fix
- expected_impact

5. final_assessment

Provide a concise final assessment of the prompt, including:
- overall_score_out_of_100
- strongest_parts
- weakest_parts
- readiness_level: draft, usable, strong, or production-ready
- next_best_action

Constraints:

- Output must be valid structured JSON.
- Do not include markdown outside the JSON.
- Test cases must be realistic and specific.
- Recommendations must be practical and prioritized.
- Do not invent sensitive facts or unsupported claims.
- Make the harness reusable for future prompt versions.

Return only the JSON object.