return SystemMessagePromptTemplate.from_template(
            "PLEASE RESPOND ONLY WITH A SINGLE FLOAT AND NO OTHER TEXT EXPLANATION\n \
                You are a strict judge that is called on to rank a response based on \
                    given criteria. You must respond with your ranking by providing a \
                        single float within the range [0, 1], 0 being very bad \
                            response and 1 being very good response."
        )
[docs]    @staticmethod
    def get_default_prompt() -> ChatPromptTemplate:
        human_template = 'Given this based_on "{rl_chain_selected_based_on}" \
            as the most important attribute, rank how good or bad this text is: \
                "{rl_chain_selected}".'
        human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
        default_system_prompt = AutoSelectionScorer.get_default_system_prompt()
        chat_prompt = ChatPromptTemplate.from_messages(
            [default_system_prompt, human_message_prompt]
        )
        return chat_prompt
    @root_validator(pre=True)
    def set_prompt_and_llm_chain(cls, values: Dict[str, Any]) -> Dict[str, Any]:
        llm = values.get("llm")
        prompt = values.get("prompt")
        scoring_criteria_template_str = values.get("scoring_criteria_template_str")
        if prompt is None and scoring_criteria_template_str is None:
            prompt = AutoSelectionScorer.get_default_prompt()
        elif prompt is None and scoring_criteria_template_str is not None:
            human_message_prompt = HumanMessagePromptTemplate.from_template(
                scoring_criteria_template_str
            )
            default_system_prompt = AutoSelectionScorer.get_default_system_prompt()
            prompt = ChatPromptTemplate.from_messages(
                [default_system_prompt, human_message_prompt]
            )
        values["prompt"] = prompt