mirror of
https://github.com/agentscope-ai/ReMe.git
synced 2026-09-23 00:43:18 +00:00
79 lines
No EOL
3.2 KiB
YAML
79 lines
No EOL
3.2 KiB
YAML
soft_comparative_step_task_memory_prompt: |
|
|
You are an expert AI analyst comparing higher-scoring and lower-scoring step sequences to extract performance insights.
|
|
|
|
Your task is to identify the key differences between higher and lower performing approaches at the step level.
|
|
Focus on what made the higher-scoring approach more effective, even when both approaches may have had partial success.
|
|
|
|
SOFT COMPARATIVE ANALYSIS FRAMEWORK:
|
|
● PERFORMANCE FACTORS: Identify what specifically contributed to the higher score
|
|
● APPROACH DIFFERENCES: Compare methodologies and execution strategies
|
|
● EFFICIENCY ANALYSIS: Analyze why one approach was more efficient or effective
|
|
● OPTIMIZATION INSIGHTS: Extract lessons for improving performance
|
|
|
|
EXTRACTION PRINCIPLES:
|
|
● Focus on INCREMENTAL IMPROVEMENTS and performance optimization
|
|
● Extract QUALITY INDICATORS that differentiate better vs good approaches
|
|
● Identify REFINEMENT STRATEGIES that lead to higher scores
|
|
● Frame insights as PERFORMANCE ENHANCEMENT guidelines
|
|
|
|
# Higher-Scoring Step Sequence (Score: {higher_score})
|
|
{higher_steps}
|
|
|
|
# Lower-Scoring Step Sequence (Score: {lower_score})
|
|
{lower_steps}
|
|
|
|
|
|
OUTPUT FORMAT:
|
|
Generate 1-2 performance improvement insights as JSON objects:
|
|
```json
|
|
[
|
|
{{
|
|
"when_to_use": "Specific scenarios where this performance insight applies",
|
|
"experience": "Detailed analysis of what made the higher-scoring approach more effective",
|
|
"tags": ["performance_optimization", "score_improvement", "relevant_keywords"],
|
|
"confidence": 0.7,
|
|
"step_type": "reasoning|action|observation|decision",
|
|
"tools_used": ["list", "of", "tools"]
|
|
}}
|
|
]
|
|
```
|
|
|
|
hard_comparative_step_task_memory_prompt: |
|
|
You are an expert AI analyst comparing successful and failed step sequences to extract differential insights.
|
|
|
|
Your task is to identify the key differences between success and failure patterns at the step level.
|
|
Focus on critical decision points, technique variations, and approach differences.
|
|
|
|
COMPARATIVE ANALYSIS FRAMEWORK:
|
|
● DECISION CONTRAST: Compare critical decisions made in success vs failure cases
|
|
● TECHNIQUE VARIATIONS: Identify different approaches and their outcomes
|
|
● TIMING DIFFERENCES: Analyze when certain actions were taken and their impact
|
|
● SUCCESS FACTORS: Extract what specifically made the difference
|
|
|
|
EXTRACTION PRINCIPLES:
|
|
● Frame comparisons as PRINCIPLES as well as case-specific SOLUTIONS
|
|
● Identify PATTERNS that differentiate effective vs ineffective approaches
|
|
● Extract RULES that can guide future similar situations
|
|
● Focus on UNDERLYING MECHANISMS rather than surface-level differences
|
|
|
|
# Successful Step Sequence
|
|
{success_steps}
|
|
|
|
# Failed Step Sequence
|
|
{failure_steps}
|
|
|
|
# Similarity Score: {similarity_score}
|
|
|
|
OUTPUT FORMAT:
|
|
Generate 1-2 comparative insights as JSON objects:
|
|
```json
|
|
[
|
|
{{
|
|
"when_to_use": "Specific scenarios where this comparative insight applies",
|
|
"experience": "Detailed comparison highlighting why success approach works better",
|
|
"tags": ["comparative_analysis", "success_factors", "relevant_keywords"],
|
|
"confidence": 0.8,
|
|
"step_type": "reasoning|action|observation|decision"
|
|
}}
|
|
]
|
|
``` |