# Coherency-over-Time (CoT) is a newer approach targeting to improve the consistency and reliability of the outputs generated by large language models. We'll assume that you'd like to implement a version of CoT for a hypothetical Large Language Model Agent (LLMAgent).

class LLMAgent:
    """
    Large Language Model Agent (LLMAgent) represents a hypothetical large language model trained on diverse data.
    """
    def __init__(self):
        self.context = []

    def respond(self, prompt: str) -> str:
        """
        The function simulates interaction with an AI model to generate a response.
        The prompt and the generated response are added to the shared context for future reference.

        Args:
            prompt (str): The input prompt for generating a response.

        Returns:
            str: The generated response.
        """
        response = "This is a simulated response based on the prompt." # Assuming this response is generated by an AI model
        self.update_context(prompt, response)
        return response

    def update_context(self, prompt: str, response: str):
        """
        Update the shared context with the latest conversational turn.

        Args:
            prompt (str): The latest input prompt.
            response (str): The latest generated response.
        """
        self.context.append({
            "role": "system",
            "content": prompt
        })
        self.context.append({
            "role": "Llama",
            "content": response
        })

    def check_coherency(self, prompt: str, response: str) -> bool:
        """
        The function that evaluates the consistenty and reliability of the generated response based on the shared context.
        This function is a placeholder and for real application, it should be replaced with a rigorous evaluation method.

        Args:
            prompt (str): The latest input prompt.
            response (str): The latest generated response.

        Returns:
            bool: The coherency evaluation result. True if the response is consistent with the context; False otherwise.
        """
        # Dummy implementation: let's assume every response is consistent with the context.
        return True

    def refine_response(self, prompt: str, initial_response: str) -> str:
        """
        Function that refines the generated response based on the shared context and coherency checks.
        This function is a placeholder; in a real application, it should be replaced with a rigorous method to refine and improve the consistency of the response.

        Args:
            prompt (str): The latest input prompt.
            initial_response (str): The initial generated response,

        Returns:
            str: The refined response.
        """
        is_coherent = self.check_coherency(prompt, initial_response)

        # In the hypothetical scenario if the initial response is not coherent, generate an alternative response
        if not is_coherent:
            refined_response = "This is a simulated refined response based on the context and initial response."
            return refined_response

        return initial_response
         
# Creating an instance of our LLM Agent
llm_agent = LLMAgent()

# Simulate a user prompt
user_prompt = "Tell me about climate change."

# The agent generates an initial response
initial_response = llm_agent.respond(user_prompt)

# The agent refines the initial response if needed
refined_response = llm_agent.refine_response(user_prompt, initial_response)

print("Refined Response: ", refined_response)

# Note: This is a conceptual implementation to demonstrate the broad idea of incorporating CoT techniques in a large language models. The functions `check_coherency` and `refine_response` are placeholders and need to be replaced with real implementations.
