wehub-resource-sync
66 lines
2.7 KiB
Python
66 lines
2.7 KiB
Python
class RewriterTemplate:
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@staticmethod
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def generate_mutation(
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original_prompt: str,
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failures: str,
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successes: str,
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results: str,
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analysis: str,
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is_list_format: bool = False,
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) -> str:
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if is_list_format:
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format_instruction = (
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"A JSON array of message objects representing the revised conversational prompt "
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"(e.g., [{'role': 'system', 'content': '...'}, {'role': 'user', 'content': '...'}])."
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)
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example_prompt = '[{"role": "system", "content": "You are a helpful assistant..."},{"role": "user", "content": "{{input}}"}]'
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else:
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format_instruction = (
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"The final string representing the optimized revised prompt."
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)
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example_prompt = '"<the optimized revised prompt here>"'
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return f"""You are an expert AI Prompt Engineer. Your goal is to perform a 'Prompt Mutation' to move the prompt closer to the Pareto Frontier.
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# Context
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- **Original Prompt:** The current best-performing candidate.
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- **Diagnostic Report:** A 'gradient' signal identifying high-loss areas (low scores) and anchors (high scores).
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- **Failure Cases:** The failure cases from the diagnostic report.
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- **Success Cases:** The success cases from the diagnostic report.
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- **Actual Results:** The actual results from the previous generation.
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- **Overall Analysis:** The overall analysis of the diagnostic report.
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# Original Prompt
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{original_prompt}
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# Diagnostic Report
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Failures: {failures}
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Successes: {successes}
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Actual results from the previous generation: {results}
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Overall analysis of the diagnostic report: {analysis}
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# Mutation Instructions
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1. **Targeted Fixes:** Use the Diagnostic Report to apply 'surgical' edits. Focus heavily on the examples that received low numerical scores.
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2. **Constraint Satisfaction:** Do NOT degrade performance on the 'Anchor' examples (those with 1.0 scores). Your mutation must be a 'non-dominated' improvement.
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3. **Preserve Placeholders:** Maintain all runtime tokens like `{{input}}` or `{{context}}`.
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4. **Iterative Refinement:** If the report mentions a lack of clarity, add explicit 'Rules' or 'Negative Constraints' (what NOT to do).
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5. Always keep the interpolation type of the prompt the same as the original prompt. We use regex to interpolate the prompt so keep the same format.
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**Output Format**
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Return a JSON object:
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- "thought_process": Explain how you are addressing the low-score failures while preserving high-score successes.
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- "revised_prompt": {format_instruction}
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Example JSON:
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{{
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"thought_process": "<your reasoning here>",
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"revised_prompt": {example_prompt}
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}}
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JSON:
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"""
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