class LLMAgent:
    """
    Large Language Model Agent (LLMAgent) represents a hypothetical large language model trained on diverse data.
    The model uses the Chain-of-Thought Prompting (CoTP) technique to generate better, more contextual responses.
    """
    def __init__(self):
        self.context = []
        self.chain_of_thought = []

    def respond(self, prompt: str) -> str:
        """
        Generates a response to a prompt by also utilizing the Chain-of-Thought (CoT) prompting method.

        :param prompt: The input prompt for generating a response.
        :return: The generated response.
        """
        # Add previous responses to the chain of thought
        if self.chain_of_thought:
            prompt += " ".join(self.chain_of_thought)
            
        response = "This is a simulated response to the prompt and chain of thought." # Assuming this response is generated by an AI model
        self.update_context(prompt, response)
        
        # Add the response to the chain of thought
        self.chain_of_thought.append(response)
        
        return response

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

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

# Create an instance of our LLM Agent
llm_agent = LLMAgent()

# Simulate a series of user prompts and the agent's responses
user_prompts = ["Tell me about climate change.", 
                "What are the causes of it?", 
                "What can we do to reduce its effects?"]

# Iterate over the user prompts and get the agent's responses
for user_prompt in user_prompts:
    print("User prompt: ", user_prompt)
    print("Agent response: ", llm_agent.respond(user_prompt), "\n")

# Note: The agent's responses should ideally be coherent and in line with the Chain-of-Thought Prompting approach. 
# This coherence is simulated here as this is just a conceptual implementation.
