mirror of
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583 lines
26 KiB
Python
583 lines
26 KiB
Python
# Standards
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SEPARATOR_LINE = "-------"
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UNKNOWN_ANSWER = "I do not have enough information to answer this question."
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NO_RECOVERED_DOCS = "No relevant information recovered"
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YES = "yes"
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NO = "no"
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# Framing/Support/Template Prompts
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HISTORY_FRAMING_PROMPT = f"""
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For more context, here is the history of the conversation so far that preceded this question:
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{SEPARATOR_LINE}
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{{history}}
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{SEPARATOR_LINE}
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""".strip()
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ASSISTANT_SYSTEM_PROMPT_DEFAULT = "You are an assistant for question-answering tasks."
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ASSISTANT_SYSTEM_PROMPT_PERSONA = f"""
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You are an assistant for question-answering tasks. Here is more information about you:
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{SEPARATOR_LINE}
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{{persona_prompt}}
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{SEPARATOR_LINE}
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""".strip()
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SUB_QUESTION_ANSWER_TEMPLATE = f"""
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Sub-Question: Q{{sub_question_num}}
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Question:
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{{sub_question}}
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{SEPARATOR_LINE}
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Answer:
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{{sub_answer}}
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""".strip()
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SUB_QUESTION_ANSWER_TEMPLATE_REFINED = f"""
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Sub-Question: Q{{sub_question_num}}
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Type: {{sub_question_type}}
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Sub-Question:
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{SEPARATOR_LINE}
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{{sub_question}}
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{SEPARATOR_LINE}
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Answer:
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{SEPARATOR_LINE}
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{{sub_answer}}
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{SEPARATOR_LINE}
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""".strip()
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# Step/Utility Prompts
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# Note this one should always be used with the ENTITY_TERM_EXTRACTION_PROMPT_JSON_EXAMPLE
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ENTITY_TERM_EXTRACTION_PROMPT = f"""
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Based on the original question and some context retrieved from a dataset, please generate a list of
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entities (e.g. companies, organizations, industries, products, locations, etc.), terms and concepts
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(e.g. sales, revenue, etc.) that are relevant for the question, plus their relations to each other.
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Here is the original question:
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{SEPARATOR_LINE}
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{{question}}
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{SEPARATOR_LINE}
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And here is the context retrieved:
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{SEPARATOR_LINE}
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{{context}}
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{SEPARATOR_LINE}
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Please format your answer as a json object in the following format:
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""".lstrip()
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ENTITY_TERM_EXTRACTION_PROMPT_JSON_EXAMPLE = """
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{
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"retrieved_entities_relationships": {
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"entities": [
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{
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"entity_name": "<assign a name for the entity>",
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"entity_type": "<specify a short type name for the entity, such as 'company', 'location',...>"
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}
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],
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"relationships": [
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{
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"relationship_name": "<assign a name for the relationship>",
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"relationship_type": "<specify a short type name for the relationship, such as 'sales_to', 'is_location_of',...>",
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"relationship_entities": ["<related entity name 1>", "<related entity name 2>", "..."]
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}
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],
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"terms": [
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{
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"term_name": "<assign a name for the term>",
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"term_type": "<specify a short type name for the term, such as 'revenue', 'market_share',...>",
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"term_similar_to": ["<list terms that are similar to this term>"]
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}
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]
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}
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}
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""".strip()
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HISTORY_CONTEXT_SUMMARY_PROMPT = (
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"{persona_specification}\n\n"
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"Your task now is to summarize the key parts of the history of a conversation between a user and an agent."
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" The summary has two purposes:\n"
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" 1) providing the suitable context for a new question, and\n"
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" 2) To capture the key information that was discussed and that the user may have a follow-up question about.\n\n"
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"Here is the question:\n"
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f"{SEPARATOR_LINE}\n"
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"{question}\n"
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f"{SEPARATOR_LINE}\n\n"
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"And here is the history:\n"
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f"{SEPARATOR_LINE}\n"
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"{history}\n"
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f"{SEPARATOR_LINE}\n\n"
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"Please provide a summarized context from the history so that the question makes sense and can"
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" - with suitable extra information - be answered.\n\n"
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"Do not use more than three or four sentences.\n\n"
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"History summary:"
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).strip()
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# INITIAL PHASE
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# Sub-question
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# Intentionally left a copy in case we want to modify this one differently
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INITIAL_QUESTION_DECOMPOSITION_PROMPT = (
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"Decompose the initial user question into no more than 3 appropriate sub-questions that help to answer the"
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" original question. The purpose for this decomposition may be to:\n"
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" 1) isolate individual entities (i.e., 'compare sales of company A and company B' ->"
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" ['what are sales for company A', 'what are sales for company B'])\n"
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" 2) clarify or disambiguate ambiguous terms (i.e., 'what is our success with company A' ->"
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" ['what are our sales with company A','what is our market share with company A',"
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" 'is company A a reference customer for us', etc.])\n"
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" 3) if a term or a metric is essentially clear, but it could relate to various components of an entity and you"
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" are generally familiar with the entity, then you can decompose the question into sub-questions that are more"
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" specific to components (i.e., 'what do we do to improve scalability of product X', 'what do we to to improve"
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" scalability of product X', 'what do we do to improve stability of product X', ...])\n"
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" 4) research an area that could really help to answer the question.\n\n"
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"Here is the initial question to decompose:\n"
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f"{SEPARATOR_LINE}\n"
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"{question}\n"
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f"{SEPARATOR_LINE}\n\n"
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"{history}\n\n"
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"Do NOT include any text in your answer outside of the list of sub-questions!"
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"Please formulate your answer as a newline-separated list of questions like so:\n"
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" <sub-question>\n"
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" <sub-question>\n"
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" <sub-question>\n"
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" ...\n\n"
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"Answer:"
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).strip()
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# TODO: combine shared pieces with INITIAL_QUESTION_DECOMPOSITION_PROMPT
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INITIAL_DECOMPOSITION_PROMPT_QUESTIONS_AFTER_SEARCH = (
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"Decompose the initial user question into no more than 3 appropriate sub-questions that help to answer the"
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" original question. The purpose for this decomposition may be to:\n"
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" 1) isolate individual entities (i.e., 'compare sales of company A and company B' ->"
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" ['what are sales for company A', 'what are sales for company B'])\n"
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" 2) clarify or disambiguate ambiguous terms (i.e., 'what is our success with company A' ->"
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" ['what are our sales with company A','what is our market share with company A',"
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" 'is company A a reference customer for us', etc.])\n"
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" 3) if a term or a metric is essentially clear, but it could relate to various components of an entity and you"
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" are generally familiar with the entity, then you can decompose the question into sub-questions that are more"
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" specific to components (i.e., 'what do we do to improve scalability of product X', 'what do we to to improve"
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" scalability of product X', 'what do we do to improve stability of product X', ...])\n"
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" 4) research an area that could really help to answer the question.\n\n"
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"To give you some context, you will see below also some documents that may relate to the question. Please only"
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" use this information to learn what the question is approximately asking about, but do not focus on the details"
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" to construct the sub-questions! Also, some of the entities, relationships and terms that are in the dataset may"
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" not be in these few documents, so DO NOT focussed too much on the documents when constructing the sub-questions!"
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" Decomposition and disambiguations are most important!\n\n"
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"Here are the sample docs to give you some context:\n"
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f"{SEPARATOR_LINE}\n"
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"{sample_doc_str}\n"
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f"{SEPARATOR_LINE}\n\n"
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"And here is the initial question to decompose:\n"
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f"{SEPARATOR_LINE}\n"
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"{question}\n"
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f"{SEPARATOR_LINE}\n\n"
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"{history}\n\n"
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"Do NOT include any text in your answer outside of the list of sub-questions!"
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"Please formulate your answer as a newline-separated list of questions like so:\n"
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" <sub-question>\n"
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" <sub-question>\n"
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" <sub-question>\n"
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" ...\n\n"
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"Answer:"
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).strip()
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# Retrieval
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QUERY_REWRITING_PROMPT = (
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"Please convert the initial user question into a 2-3 more appropriate short and pointed search queries for"
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" retrieval from a document store. Particularly, try to think about resolving ambiguities and make the search"
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" queries more specific, enabling the system to search more broadly.\n"
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"Also, try to make the search queries not redundant, i.e. not too similar!\n\n"
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"Here is the initial question:\n"
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f"{SEPARATOR_LINE}\n"
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"{question}\n"
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f"{SEPARATOR_LINE}\n\n"
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"Do NOT include any text in your answer outside of the list of queries!"
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"Formulate the queries separated by newlines (Do not say 'Query 1: ...', just write the querytext) as follows:\n"
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"<query 1>\n"
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"<query 2>\n"
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"...\n\n"
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"Queries:"
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)
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DOCUMENT_VERIFICATION_PROMPT = (
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"Determine whether the following document text contains data or information that is potentially relevant "
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"for a question. It does not have to be fully relevant, but check whether it has some information that "
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"would help - possibly in conjunction with other documents - to address the question.\n\n"
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"Be careful that you do not use a document where you are not sure whether the text applies to the objects "
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"or entities that are relevant for the question. For example, a book about chess could have long passage "
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"discussing the psychology of chess without - within the passage - mentioning chess. If now a question "
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"is asked about the psychology of football, one could be tempted to use the document as it does discuss "
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"psychology in sports. However, it is NOT about football and should not be deemed relevant. Please "
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"consider this logic.\n\n"
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"DOCUMENT TEXT:\n"
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f"{SEPARATOR_LINE}\n"
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"{document_content}\n"
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f"{SEPARATOR_LINE}\n\n"
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"Do you think that this document text is useful and relevant to answer the following question?\n\n"
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"QUESTION:\n"
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f"{SEPARATOR_LINE}\n"
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"{question}\n"
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f"{SEPARATOR_LINE}\n\n"
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"Please answer with exactly and only a 'yes' or 'no'. Do NOT include any other text in your response:\n\n"
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"Answer:"
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).strip()
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# Sub-Question Anser Generation
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SUB_QUESTION_RAG_PROMPT = (
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"Use the context provided below - and only the provided context - to answer the given question. "
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"(Note that the answer is in service of answering a broader question, given below as 'motivation'.)\n\n"
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"Again, only use the provided context and do not use your internal knowledge! If you cannot answer the "
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f'question based on the context, say "{UNKNOWN_ANSWER}". It is a matter of life and death that you do NOT '
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"use your internal knowledge, just the provided information!\n\n"
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"Make sure that you keep all relevant information, specifically as it concerns to the ultimate goal. "
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"(But keep other details as well.)\n\n"
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"It is critical that you provide inline citations in the format [D1], [D2], [D3], etc! "
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"It is important that the citation is close to the information it supports. "
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"Proper citations are very important to the user!\n\n"
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"For your general information, here is the ultimate motivation:\n"
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f"{SEPARATOR_LINE}\n"
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"{original_question}\n"
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f"{SEPARATOR_LINE}\n\n"
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"And here is the actual question I want you to answer based on the context above (with the motivation in mind):\n"
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f"{SEPARATOR_LINE}\n"
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"{question}\n"
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f"{SEPARATOR_LINE}\n\n"
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"Here is the context:\n"
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f"{SEPARATOR_LINE}\n"
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"{context}\n"
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f"{SEPARATOR_LINE}\n\n"
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"Please keep your answer brief and concise, and focus on facts and data.\n\n"
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"Answer:"
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).strip()
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SUB_ANSWER_CHECK_PROMPT = (
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"Determine whether the given answer addresses the given question. "
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"Please do not use any internal knowledge you may have - just focus on whether the answer "
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"as given seems to largely address the question as given, or at least addresses part of the question.\n\n"
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"Here is the question:\n"
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f"{SEPARATOR_LINE}\n"
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"{question}\n"
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f"{SEPARATOR_LINE}\n\n"
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"Here is the suggested answer:\n"
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f"{SEPARATOR_LINE}\n"
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"{base_answer}\n"
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f"{SEPARATOR_LINE}\n\n"
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f'Does the suggested answer address the question? Please answer with "{YES}" or "{NO}".'
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).strip()
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# Initial Answer Generation
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INITIAL_ANSWER_PROMPT_W_SUB_QUESTIONS = (
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"{persona_specification}\n\n"
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"Use the information provided below - and only the provided information - to answer the provided main question.\n\n"
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"The information provided below consists of:\n"
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" 1) a number of answered sub-questions - these are very important to help you organize your thoughts and your answer\n"
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" 2) a number of documents that deemed relevant for the question.\n\n"
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"{history}\n\n"
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"It is critical that you provide prover inline citations to documents in the format [D1], [D2], [D3], etc.!\n"
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"It is important that the citation is close to the information it supports. If you have multiple citations that support\n"
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"a fact, please cite for example as [D1][D3], or [D2][D4], etc.\n"
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"Feel free to also cite sub-questions in addition to documents, but make sure that you have documents cited with the "
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"sub-question citation. If you want to cite both a document and a sub-question, please use [D1][Q3], or "
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"[D2][D7][Q4], etc.\n"
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"Again, please NEVER cite sub-questions without a document citation! "
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"Proper citations are very important for the user!\n\n"
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"IMPORTANT RULES:\n"
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" - If you cannot reliably answer the question solely using the provided information, say that you cannot reliably answer.\n"
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" You may give some additional facts you learned, but do not try to invent an answer.\n"
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f' - If the information is empty or irrelevant, just say "{UNKNOWN_ANSWER}".\n'
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" - If the information is relevant but not fully conclusive, specify that the information is not conclusive and say why.\n\n"
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"Again, you should be sure that the answer is supported by the information provided!\n\n"
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"Try to keep your answer concise. But also highlight uncertainties you may have should there be substantial ones,\n"
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"or assumptions you made.\n\n"
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"Here is the contextual information:\n"
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"---------------\n\n"
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"*Answered Sub-questions (these should really matter!):\n"
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f"{SEPARATOR_LINE}\n"
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"{answered_sub_questions}\n"
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f"{SEPARATOR_LINE}\n\n"
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"And here are relevant document information that support the sub-question answers, "
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"or that are relevant for the actual question:\n"
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f"{SEPARATOR_LINE}\n"
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"{relevant_docs}\n"
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f"{SEPARATOR_LINE}\n\n"
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"And here is the question I want you to answer based on the information above:\n"
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f"{SEPARATOR_LINE}\n"
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"{question}\n"
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f"{SEPARATOR_LINE}\n\n"
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"Please keep your answer brief and concise, and focus on facts and data.\n\n"
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"Answer:"
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).strip()
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# Used if sub_question_answer_str is empty
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INITIAL_ANSWER_PROMPT_WO_SUB_QUESTIONS = (
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"{answered_sub_questions}{persona_specification}\n\n"
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"Use the information provided below - and only the provided information - to answer the provided question. "
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"The information provided below consists of a number of documents that were deemed relevant for the question.\n"
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"{history}\n\n"
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"IMPORTANT RULES:\n"
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" - If you cannot reliably answer the question solely using the provided information, say that you cannot reliably answer. "
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"You may give some additional facts you learned, but do not try to invent an answer.\n"
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f' - If the information is irrelevant, just say "{UNKNOWN_ANSWER}".\n'
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" - If the information is relevant but not fully conclusive, specify that the information is not conclusive and say why.\n\n"
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"Again, you should be sure that the answer is supported by the information provided!\n\n"
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"It is critical that you provide proper inline citations to documents in the format [D1], [D2], [D3], etc! "
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"It is important that the citation is close to the information it supports. If you have multiple citations, "
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"please cite for example as [D1][D3], or [D2][D4], etc. Citations are very important for the user!\n\n"
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"Here is the relevant context information:\n"
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f"{SEPARATOR_LINE}\n"
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"{relevant_docs}\n"
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f"{SEPARATOR_LINE}\n\n"
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"And here is the question I want you to answer based on the context above:\n"
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f"{SEPARATOR_LINE}\n"
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"{question}\n"
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f"{SEPARATOR_LINE}\n\n"
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"Please keep your answer brief and concise, and focus on facts and data.\n\n"
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"Answer:"
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).strip()
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# REFINEMENT PHASE
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REFINEMENT_QUESTION_DECOMPOSITION_PROMPT = (
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"An initial user question needs to be answered. An initial answer has been provided but it wasn't quite "
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"good enough. Also, some sub-questions had been answered and this information has been used to provide "
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"the initial answer. Some other subquestions may have been suggested based on little knowledge, but they "
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"were not directly answerable. Also, some entities, relationships and terms are given to you so that "
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"you have an idea of how the available data looks like.\n\n"
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"Your role is to generate 2-4 new sub-questions that would help to answer the initial question, considering:\n\n"
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"1) The initial question\n"
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"2) The initial answer that was found to be unsatisfactory\n"
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"3) The sub-questions that were answered\n"
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"4) The sub-questions that were suggested but not answered\n"
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"5) The entities, relationships and terms that were extracted from the context\n\n"
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"The individual questions should be answerable by a good RAG system. "
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"So a good idea would be to use the sub-questions to resolve ambiguities and/or to separate the "
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"question for different entities that may be involved in the original question, but in a way that does "
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"not duplicate questions that were already tried.\n\n"
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"Additional Guidelines:\n"
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"- The sub-questions should be specific to the question and provide richer context for the question, "
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"resolve ambiguities, or address shortcoming of the initial answer\n"
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"- Each sub-question - when answered - should be relevant for the answer to the original question\n"
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"- The sub-questions should be free from comparisons, ambiguities,judgements, aggregations, or any "
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"other complications that may require extra context.\n"
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"- The sub-questions MUST have the full context of the original question so that it can be executed by "
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"a RAG system independently without the original question available\n"
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" (Example:\n"
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' - initial question: "What is the capital of France?"\n'
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' - bad sub-question: "What is the name of the river there?"\n'
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' - good sub-question: "What is the name of the river that flows through Paris?")\n'
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"- For each sub-question, please also provide a search term that can be used to retrieve relevant "
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"documents from a document store.\n"
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"- Consider specifically the sub-questions that were suggested but not answered. This is a sign that they are not "
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"answerable with the available context, and you should not ask similar questions.\n\n"
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"Here is the initial question:\n"
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f"{SEPARATOR_LINE}\n"
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"{question}\n"
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f"{SEPARATOR_LINE}\n"
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"{history}\n\n"
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"Here is the initial sub-optimal answer:\n"
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f"{SEPARATOR_LINE}\n"
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"{base_answer}\n"
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f"{SEPARATOR_LINE}\n\n"
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"Here are the sub-questions that were answered:\n"
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f"{SEPARATOR_LINE}\n"
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"{answered_sub_questions}\n"
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f"{SEPARATOR_LINE}\n\n"
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"Here are the sub-questions that were suggested but not answered:\n"
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f"{SEPARATOR_LINE}\n"
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"{failed_sub_questions}\n"
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f"{SEPARATOR_LINE}\n\n"
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"And here are the entities, relationships and terms extracted from the context:\n"
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f"{SEPARATOR_LINE}\n"
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"{entity_term_extraction_str}\n"
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f"{SEPARATOR_LINE}\n\n"
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"Please generate the list of good, fully contextualized sub-questions that would help to address the main question.\n"
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"Specifically pay attention also to the entities, relationships and terms extracted, as these indicate what type of "
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"objects/relationships/terms you can ask about! Do not ask about entities, terms or relationships that are not "
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"mentioned in the 'entities, relationships and terms' section.\n\n"
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"Again, please find questions that are NOT overlapping too much with the already answered "
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"sub-questions or those that already were suggested and failed.\n"
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"In other words - what can we try in addition to what has been tried so far?\n\n"
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"Generate the list of questions separated by one new line like this:\n"
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"<sub-question 1>\n"
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"<sub-question 2>\n"
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"<sub-question 3>\n"
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"..."
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).strip()
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REFINED_ANSWER_PROMPT_W_SUB_QUESTIONS = (
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"""\n
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{persona_specification}
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|
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Your task is to improve on a given answer to a question, as the initial answer was found to be lacking in some way.
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|
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Use the information provided below - and only the provided information - to write your new and improved answer.
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|
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The information provided below consists of:
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1) an initial answer that was given but found to be lacking in some way.
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2) a number of answered sub-questions - these are very important(!) and definitely should help you to answer
|
|
the main question. Note that the sub-questions have a type, 'initial' and 'refined'. The 'initial'
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ones were available for the creation of the initial answer, but the 'refined' were not, they are new. So please use
|
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the 'refined' sub-questions in particular to update/extend/correct/enrich the initial answer and to add
|
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more details/new facts!
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|
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3) a number of documents that were deemed relevant for the question. This the is the context that you use largely for
|
|
citations (see below). So consider the answers to the sub-questions as guidelines to construct your new answer, but
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make sure you cite the relevant document for a fact!
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|
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|
It is critical that you provide proper inline citations to documents in the format [D1], [D2], [D3], etc!
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|
It is important that the citation is close to the information it supports. If you have multiple
|
|
citations, please cite for example as [D1][D3], or [D2][D4], etc.
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|
Feel free to also cite sub-questions in addition to documents, but make sure that you have documents cited with the sub-question
|
|
citation. If you want to cite both a document and a sub-question, please use [D1][Q3], or [D2][D7][Q4], etc.
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Again, please NEVER cite sub-questions without a document citation!
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|
Proper citations are very important for the user!\n\n
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|
|
|
{history}
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|
|
|
IMPORTANT RULES:
|
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- If you cannot reliably answer the question solely using the provided information, say that you cannot reliably answer.
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You may give some additional facts you learned, but do not try to invent an answer.
|
|
- If the information is empty or irrelevant, just say """
|
|
+ f'"{UNKNOWN_ANSWER}"'
|
|
+ """.
|
|
- If the information is relevant but not fully conclusive, provide an answer to the extent you can but also
|
|
specify that the information is not conclusive and why.
|
|
- Ignore any existing citations within the answered sub-questions, like [D1]... and [Q2]!
|
|
The citations you will need to use will need to refer to the documents (and sub-questions) that you are explicitly
|
|
presented with below!
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|
|
|
Again, you should be sure that the answer is supported by the information provided!
|
|
|
|
Try to keep your answer concise. But also highlight uncertainties you may have should there be substantial ones,
|
|
or assumptions you made.
|
|
|
|
Here is the contextual information:
|
|
---------------
|
|
|
|
*Initial Answer that was found to be lacking:
|
|
\n-------\n
|
|
{initial_answer}
|
|
\n-------\n
|
|
|
|
*Answered Sub-questions (these should really help you to research your answer! They also contain questions/answers
|
|
that were not available when the original answer was constructed):
|
|
{answered_sub_questions}
|
|
|
|
And here are the relevant documents that support the sub-question answers, and that are relevant for the actual question:\n
|
|
\n-------\n
|
|
{relevant_docs}
|
|
\n-------\n
|
|
|
|
\n
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|
Lastly, here is the main question I want you to answer based on the information above:
|
|
\n-------\n
|
|
{question}
|
|
\n-------\n
|
|
|
|
Please keep your answer brief and concise, and focus on facts and data.
|
|
|
|
Answer:"""
|
|
)
|
|
|
|
# sub_question_answer_str is empty
|
|
REFINED_ANSWER_PROMPT_WO_SUB_QUESTIONS = (
|
|
"""\n
|
|
{answered_sub_questions}\n
|
|
{persona_specification}
|
|
|
|
Use the information provided below - and only the
|
|
provided information - to answer the provided question.
|
|
|
|
The information provided below consists of:
|
|
1) an initial answer that was given but found to be lacking in some way.
|
|
2) a number of documents that were also deemed relevant for the question.
|
|
|
|
It is critical that you provide proper] inline citations to documents in the format [D1], [D2], [D3], etc!
|
|
It is important that the citation is close to the information it supports. If you have multiple
|
|
citations, please cite for example as [D1][D3], or [D2][D4], etc. Citations are very important for the user!\n\n
|
|
\n-------\n
|
|
{history}
|
|
\n-------\n
|
|
IMPORTANT RULES:
|
|
- If you cannot reliably answer the question solely using the provided information, say that you cannot reliably answer.
|
|
You may give some additional facts you learned, but do not try to invent an answer.
|
|
- If the information is empty or irrelevant, just say """
|
|
+ f'"{UNKNOWN_ANSWER}"'
|
|
+ """.
|
|
- If the information is relevant but not fully conclusive, provide and answer to the extent you can but also
|
|
specify that the information is not conclusive and why.
|
|
|
|
Again, you should be sure that the answer is supported by the information provided!
|
|
|
|
Try to keep your answer concise. But also highlight uncertainties you may have should there be substantial ones,
|
|
or assumptions you made.
|
|
|
|
Here is the contextual information:
|
|
---------------
|
|
|
|
*Initial Answer that was found to be lacking:
|
|
\n-------\n
|
|
{initial_answer}
|
|
\n-------\n
|
|
|
|
And here are relevant document information that support the sub-question answers, or that are relevant for the actual question:\n
|
|
\n-------\n
|
|
{relevant_docs}
|
|
\n-------\n
|
|
\n
|
|
Lastly, here is the question I want you to answer based on the information above:
|
|
\n-------\n
|
|
{question}
|
|
\n-------\n\n
|
|
Please keep your answer brief and concise, and focus on facts and data.
|
|
|
|
Answer:"""
|
|
)
|
|
|
|
|
|
INITIAL_REFINED_ANSWER_COMPARISON_PROMPT = """
|
|
For the given question, please compare the initial answer and the refined answer and determine if
|
|
the refined answer is substantially better than the initial answer, not just a bit better. Better could mean:
|
|
- additional information
|
|
- more comprehensive information
|
|
- more concise information
|
|
- more structured information
|
|
- morde details
|
|
- new bullet points
|
|
- substantially more document citations ([D1], [D2], [D3], etc.)
|
|
|
|
Put yourself in the shoes of the user and think about whether the refined answer is really substantially
|
|
better and delivers really new insights than the initial answer.
|
|
|
|
Here is the question:
|
|
\n-------\n
|
|
{question}
|
|
\n-------\n
|
|
|
|
Here is the initial answer:
|
|
\n-------\n
|
|
{initial_answer}
|
|
\n-------\n
|
|
|
|
Here is the refined answer:
|
|
\n-------\n
|
|
{refined_answer}
|
|
\n-------\n
|
|
|
|
With these criteria in mind, is the refined answer substantially better than the initial answer?
|
|
|
|
Please answer with a simple 'yes' or 'no'.
|
|
"""
|