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Meta-Prompt

Meta-Prompt

A collection of meta-prompting techniques for evaluating and analyzing AI responses and solution paths.
这是一套用于评估和分析AI响应与解决路径的元提示（Meta-prompting）技术集合。

Response Quality Evaluator

响应质量评估器

A framework for critiquing and reflecting on the quality of responses, providing a score and indicating whether the response has fully solved the question or task.
一个用于评判和反思响应质量的框架，提供评分并标记响应是否完全解决了对应问题或任务。

Evaluation Fields

评估维度

Reflections: The critique and reflections on the sufficiency, superfluency, and general quality of the response.
Score: Score from 0-10 on the quality of the candidate response.
Found_solution: Whether the response has fully solved the question or task.
反思与评判：对响应的充分性、冗余度及整体质量的评判与反思。
评分：对候选响应的质量给出0-10分的评分。
是否解决问题：响应是否完全解决了对应的问题或任务。

Evaluation Criteria

评估标准

When evaluating responses, consider the following:
  1. Accuracy: Does the response correctly address the question or task?
  2. Completeness: Does it cover all aspects of the question or task?
  3. Clarity: Is the response clear and easy to understand?
  4. Conciseness: Is the response appropriately concise without sacrificing important details?
  5. Relevance: Does the response stay focused on the question or task at hand?
Provide thoughtful reflections on these aspects and any other relevant factors. Use the score to indicate the overall quality, and set found_solution to true only if the response fully addresses the question or completes the task.
评估响应时，请考虑以下要点：
  1. 准确性：响应是否正确对应问题或任务要求？
  2. 完整性：是否覆盖了问题或任务的所有方面？
  3. 清晰度：响应是否清晰易懂？
  4. 简洁性：是否在不牺牲重要细节的前提下做到了适当简洁？
  5. 相关性：是否始终聚焦于当前的问题或任务？
请针对这些方面及其他相关因素给出有深度的反思。用评分表示整体质量，仅当响应完全解决问题或完成任务时，将found_solution设为true。

Example Usage

示例用法

reflections: "The response was clear and concise, addressing the main question effectively. However, it could have provided more context on edge cases."
score: 8
found_solution: true
reflections: "The response was clear and concise, addressing the main question effectively. However, it could have provided more context on edge cases."
score: 8
found_solution: true

Question-Answering Trajectory Analyzer

问答路径分析器

Guidelines for analyzing solution paths to question-answering tasks.
用于分析问答类任务解决路径的指导准则。

Trajectory Components

路径组成部分

Observations: Environmental information about the current situation that provides context for decision-making.
Thoughts: Reasoning about the current situation, analyzing what has been observed and planning next steps.
Actions: The steps taken to progress toward solving the task.
观测信息：为决策提供上下文的当前环境信息。
思考过程：对当前情况的推理，分析已观测信息并规划下一步行动。
行动步骤：为推进任务解决所采取的步骤。

Action Types

行动类型

Search[entity]: Searches for the exact entity and returns relevant information if the entity exists. If not, returns suggestions for similar entities.
Lookup[keyword]: Returns the next relevant passage that contains the keyword. Used for finding specific information within retrieved content.
Finish[answer]: Returns the answer and finishes the task. Used when sufficient information has been gathered to provide a definitive response.
Search[entity]：搜索指定实体，若实体存在则返回相关信息；若不存在则返回相似实体建议。
Lookup[keyword]：返回包含该关键词的下一个相关段落，用于在已检索内容中查找特定信息。
Finish[answer]：返回答案并结束任务，适用于已收集到足够信息可给出明确响应的场景。

Analysis Guidelines

分析准则

When analyzing a trajectory:
  1. Evaluate whether each observation provides useful information
  2. Assess if thoughts demonstrate logical reasoning
  3. Determine if actions are appropriate given the current state
  4. Score the trajectory correctness from 1-10
  5. Evaluate reasoning validity even in incomplete trajectories
  6. Do not generate additional steps; only analyze what is provided
分析路径时：
  1. 评估每个观测信息是否提供了有用内容
  2. 评估思考过程是否展现了逻辑推理
  3. 判断行动步骤是否适应当前状态
  4. 对路径的正确性给出1-10分的评分
  5. 即使路径不完整，也要评估推理的有效性
  6. 不要生成额外步骤，仅分析已提供的内容

Prompt Engineering Patterns

提示工程模式

Chain of Thought

思维链（Chain of Thought）

Guide the model through step-by-step reasoning:
Let's approach this step by step:
1. First, identify the key components
2. Then, analyze each component
3. Finally, synthesize the findings
引导模型逐步进行推理：
Let's approach this step by step:
1. First, identify the key components
2. Then, analyze each component
3. Finally, synthesize the findings

Few-Shot Learning

少样本学习（Few-Shot Learning）

Provide examples to establish the pattern:
Example 1: [input] -> [output]
Example 2: [input] -> [output]
Now apply this pattern to: [new input]
提供示例以建立模式：
Example 1: [input] -> [output]
Example 2: [input] -> [output]
Now apply this pattern to: [new input]

Self-Consistency

自一致性（Self-Consistency）

Generate multiple reasoning paths and select the most consistent answer.
生成多条推理路径并选择最一致的答案。

Reflection Prompts

反思提示（Reflection Prompts）

Encourage self-critique:
Review your response and identify:
- Any potential errors or oversights
- Areas that could be explained more clearly
- Missing information that would strengthen the answer
鼓励自我评判：
Review your response and identify:
- Any potential errors or oversights
- Areas that could be explained more clearly
- Missing information that would strengthen the answer

Quality Metrics

质量指标

Response Scoring Rubric

响应评分准则

  • 10: Perfect response, fully addresses all aspects with exceptional clarity
  • 8-9: Excellent response with minor room for improvement
  • 6-7: Good response that addresses the main points but lacks depth
  • 4-5: Adequate response with significant gaps or unclear explanations
  • 2-3: Poor response that misses key aspects or contains errors
  • 0-1: Response fails to address the question or is completely incorrect
  • 10分：完美响应，清晰全面地覆盖所有方面
  • 8-9分：优秀响应，仅存在微小的改进空间
  • 6-7分：良好响应，覆盖了核心要点但缺乏深度
  • 4-5分：合格响应，存在明显的信息缺口或解释模糊
  • 2-3分：较差响应，遗漏关键内容或存在错误
  • 0-1分：完全未响应问题或内容完全错误

Trajectory Scoring

路径评分

  • 10: Optimal path with efficient, logical steps
  • 7-9: Good path with minor inefficiencies
  • 4-6: Acceptable path but with unnecessary steps or missed opportunities
  • 1-3: Poor path with fundamental reasoning errors
  • 10分：最优路径，步骤高效且逻辑严谨
  • 7-9分：良好路径，仅存在微小的低效问题
  • 4-6分：可接受路径，但存在不必要的步骤或错失优化机会
  • 1-3分：较差路径，存在根本性的推理错误