摘要：在我们之前对AI代理架构的探索中，我们讨论了角色、指令和记忆等核心组件。现在，我们将深入探讨不同的提示策略如何增强代理的推理能力，使其在解决问题时更加有条理和透明。
在我们之前对AI代理架构的探索中，我们讨论了角色、指令和记忆等核心组件。现在，我们将深入探讨不同的提示策略如何增强代理的推理能力，使其在解决问题时更加有条理和透明。
有效的提示工程技术已被证明在帮助大型语言模型（LLMs）生成更可靠、结构化和合理推理的响应方面至关重要。这些技术利用了以下几个关键原则：
逐步分解：将复杂任务分解为更小、可管理的步骤，帮助LLMs更系统地处理信息，减少错误并提高逻辑一致性。明确的格式指令：提供清晰的输出结构，指导模型组织其思维并以更易消化的格式呈现信息。自我反思提示：鼓励模型审查自己的推理过程，帮助捕捉潜在错误并考虑替代视角。上下文框架：提供特定的框架（如“分析利弊”或“考虑多种情景”），帮助模型从不同角度处理问题。这些技术构成了我们实现的推理策略的基础，每种策略都旨在利用LLM能力的不同方面，同时保持响应的一致性和可靠性。
虽然基础代理可以直接处理任务，但高级推理需要结构化的解决问题方法。该实现使用策略模式来定义不同的推理框架。让我们看看这些策略在我们增强的代理架构中是如何定义的：
class ExecutionStrategy(ABC):@abstractmethoddef build_prompt(self, task: str, instruction: Optional[str] = None) -> str:"""Build the prompt according to the strategy."""pass@abstractmethoddef process_response(self, response: str) -> str:"""Process the LLM response according to the strategy."""pass这个抽象基类为实现各种推理策略提供了基础。每种策略都提供了独特的方法来：
结构化问题解决过程；分解复杂任务；组织代理的思维过程；确保对问题的全面考虑。让我们仔细看看三种不同的技术：ReAct、Chain of Thought和Reflection。该框架也便于添加其他技术。
ReAct策略（Reasoning and Action）实现了思考、行动和观察的循环，使代理的决策过程变得明确且可追踪。以下是其实现方式：
class ReactStrategy(ExecutionStrategy):def build_prompt(self, task: str, instruction: Optional[str] = None) -> str:base_prompt = """Approach this task using the following steps:1) Thought: Analyze what needs to be done2) Action: Decide on the next action3) Observation: Observe the result4) Repeat until task is completeFollow this format for your response:Thought: [Your reasoning about the current situation]Action: [The action you decide to take]Observation: [What you observe after the action]... (continue steps as needed)Final Answer: [Your final response to the task]Task: {task}"""该策略确保：
Chain of Thought策略将复杂问题分解为可管理的步骤，使推理过程更加透明和可验证。以下是其实现：
class ChainOfThoughtStrategy(ExecutionStrategy):def build_prompt(self, task: str, instruction: Optional[str] = None) -> str:base_prompt = """Let's solve this step by step:Task: {task}Please break down your thinking into clear steps:1) First, ...2) Then, ...(continue with your step-by-step reasoning)Final Answer: [Your conclusion based on the above reasoning]"""该方法提供了：
复杂问题的线性进展；步骤与结论之间的清晰联系；更容易验证推理过程；更好地理解结论是如何得出的。Reflection策略增加了一个元认知层，鼓励代理审查自己的假设并考虑替代方法。以下是其代码实现：
class ReflectionStrategy(ExecutionStrategy):def build_prompt(self, task: str, instruction: Optional[str] = None) -> str:base_prompt = """Complete this task using reflection:Task: {task}1) Initial Approach:- What is your first impression of how to solve this?- What assumptions are you making?2) Analysis:- What could go wrong with your initial approach?- What alternative approaches could you consider?3) Refined Solution:- Based on your reflection, what is the best approach?- Why is this approach better than the alternatives?"""这些策略通过工厂模式和策略设置器无缝集成到代理架构中：
class Agent:@propertydef strategy(self) -> Optional[ExecutionStrategy]:return self._strategy@strategy.setterdef strategy(self, strategy_name: str):"""Set the execution strategy by name."""self._strategy = StrategyFactory.create_strategy(strategy_name)执行流程结合了所选策略：
def execute(self, task: Optional[str] = None) -> str:if task is not None:self._task = taskmessages = self._build_messagestry:response = client.chat.completions.create(model=self._model,messages=messages)response_content = response.choices[0].message.content# Process response through strategy if setif self._strategy:response_content = self._strategy.process_response(response_content)6、实际应用以下是这些策略在实际中的应用示例：
from agent import Agentdef main:# Initialize the agentagent = Agent("Problem Solver")# Configure the agentagent.persona = """You are an analytical problem-solving assistant.You excel at breaking down complex problems and explaining your thought process.You are thorough, logical, and clear in your explanations."""agent.instruction = "Ensure your responses are clear, detailed, and well-structured."# Define the park planning taskpark_planning_task = """A city is planning to build a new park. They have the following constraints:- Budget: $2 million- Space: 5 acres- Must include: playground, walking trails, and parking- Environmental concerns: preserve existing trees- Community request: include area for community eventsHow should they approach this project?"""# Display available reasoning strategiesprint("Available reasoning strategies:", agent.available_strategies)print("\n" + "="*50)# Test ReAct strategyprint("\n=== Using ReAct Strategy ===")agent.strategy = "ReactStrategy"agent.task = park_planning_taskresponse = agent.executeprint(f"\nTask: {park_planning_task}")print("\nResponse:")print(response)print("\n" + "="*50)# Test Chain of Thought strategyprint("\n=== Using Chain of Thought Strategy ===")agent.clear_history # Clear previous interaction historyagent.strategy = "ChainOfThoughtStrategy"agent.task = park_planning_taskresponse = agent.executeprint(f"\nTask: {park_planning_task}")print("\nResponse:")print(response)print("\n" + "="*50)# Test Reflection strategyprint("\n=== Using Reflection Strategy ===")agent.clear_history # Clear previous interaction historyagent.strategy = "ReflectionStrategy"agent.task = park_planning_taskresponse = agent.executeprint(f"\nTask: {park_planning_task}")print("\nResponse:")print(response)print("\n" + "="*50)if __name__ == "__main__":main该实现允许：
灵活的策略选择：针对不同类型的任务采用不同的推理方法。一致的格式：无论选择哪种策略，输出都保持结构化。清晰的推理轨迹：透明记录问题解决过程。策略比较：轻松评估同一问题的不同解决方法。7、结束语这些推理策略的实施带来了几个关键优势：
整个框架的源代码可在GitHub仓库中找到。
基于任务类型的动态策略选择；结合多种策略的混合方法；增强每个策略中的错误处理；基于指标的策略有效性评估。结构化推理策略与代理现有能力的结合，创造了一个更强大、更通用的系统，能够处理复杂问题，同时保持决策过程的透明性和可靠性。
来源：晓晨论科技
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