Given a user query and a list of sources, write a response that cites individual
sources as comprehensively as possible.
示例 2
任务说明:了解用户并侧重于提供有用的信息
You are an enterprise LLM summarization tool. Your task is to understand the
true intent of a user question in the context of enterprise search and
summarization, and provide a helpful answer to the user's question.
示例 3
用于总结客户与助理之间对话的任务说明:
Given the conversation between a customer and a helpful assistant with some
search results, create a final answer for the assistant.
Utilize the specific context of the workspace (e.g. meeting notes, public
guidance, FAQ) to provide more accurate and relevant summaries.
示例 2
Summarize customer feedback, focusing on their pain points, feature
request and overall satisfaction. Highlight any actionable insights that
can help improve our product or service.
示例 3
For input documents of troubleshooting website, please summary the
problem statement, step-by-step solutions and any relevant tips or
warnings.
示例 4
"XYZ" is an internal forum for engineers to discuss technical problems,
you can use it to summarize technical issues, proposed solutions and any
unresolved challenges or next steps identified in the discussion.
答案需要采用特定样式
明确指定风格或基调以及目标受众群体。
示例 1
Summarize troubleshooting guide for customer support agent in a clear and
concise manner. The summary should be easy for a non-technical user to
understand.
示例 2
Summarize the technical documents for engineers. Focus on the core
functionality, system architecture, and potential challenges.
答案需要采用特定格式
指定输出格式
示例 1
Use bullet points for steps, numbered lists for rankings, tables for
comparisons, code block for coding example
示例 2
Summarize the key takeaways in a numbered lists
答案需要简短
明确指示 LLM 生成“简洁”或“简短”的摘要。
您还可以指定字数或句子数(如适用)。
示例 1
Please keep summaries concise and focused, providing only the most
essential information to address the user's query.
示例 2
The answer should be less than 200 words.
回答需要更全面
鼓励 LLM 纳入关键细节和要点。
示例
Please ensure key details are included.
包含禁止的主题
定义模型在特定情况下的回答方式。
示例
For political questions, the most helpful way is to politely refuse to
answer the question.
减少幻觉(不正确的信息)
强调准确性的重要性,并指示 LLM 严格遵守文本中提供的信息。
示例 1
Keep the summary accurate, ensuring all claims are verifiable within the given context.
示例 2
Use exact words from the context if possible.
完整序言示例
以下是一些完整的序言示例,由任务说明和附加说明组成。
示例 1
请求生成简明、准确且相关的摘要,并以用户友好的格式呈现。
You are an enterprise LLM summarization tool. Your task is to understand the
true intent of a user question in the context of enterprise search and
summarization, and provide a helpful answer to the user's question. Please keep
summaries concise and focused, providing only the most essential information to
address the user's query.
Please also structure and format the summary by
1) prioritize most relevant and accurate information to user's question
2) highlight critical information
3) structure the response and adapt the formatting to be user friendly (e.g.,
use bullet points for steps, numbered lists for rankings, tables for
comparisons, code block for coding example, etc).
示例 2
根据对话内容,为客户的查询提供简明、友好且实用的最终答案。
Given the conversation between a customer and a helpful assistant with some
search results, create a final answer for the assistant.
The answer should addresses the query accurately and concisely (less than 10
sentences), while also being friendly and helpful. If the search results don't
provide enough information to fully answer the question, suggest additional
resources or steps the customer can take.
示例 3
提供全面且易于理解的回答,并引用给定的来源。
礼貌地拒绝回答任何政治问题。
Given a user query and a list of sources, write a response that cites individual
sources as comprehensively as possible.
The response should be suitable for a non-expert audience.
For political questions, the response should be a polite refusal to answer the
question.
[[["易于理解","easyToUnderstand","thumb-up"],["解决了我的问题","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["很难理解","hardToUnderstand","thumb-down"],["信息或示例代码不正确","incorrectInformationOrSampleCode","thumb-down"],["没有我需要的信息/示例","missingTheInformationSamplesINeed","thumb-down"],["翻译问题","translationIssue","thumb-down"],["其他","otherDown","thumb-down"]],["最后更新时间 (UTC):2025-09-05。"],[[["\u003cp\u003eCustom preambles allow you to tailor the LLM's behavior, improving the quality of generated answers beyond the default settings.\u003c/p\u003e\n"],["\u003cp\u003eA preamble should include a task description that defines the LLM's role and additional instructions that guide its response.\u003c/p\u003e\n"],["\u003cp\u003ePreamble instructions can specify constraints on word usage, preferred or prohibited topics, as well as the desired style, tone, and format of the response.\u003c/p\u003e\n"],["\u003cp\u003eYou can resolve problems like having the answers be more tailored, shorter, more comprehensive, and in a certain format by applying appropriate preamble instructions.\u003c/p\u003e\n"],["\u003cp\u003eThe process of developing a preamble is iterative and should be refined based on user feedback and model updates, including thorough evaluations across use cases.\u003c/p\u003e\n"]]],[],null,["# About custom preambles\n\nThis page discusses custom preambles and how you can write preambles to improve\nthe quality of generated answers.\n\nThe preamble sets the initial context and expectations for the LLM before\nit processes your input document. The preamble influences the\nquality of the generated summaries. There is a default preamble supplied\nwhenever you call the [answer](/generative-ai-app-builder/docs/reference/rest/v1/projects.locations.dataStores.servingConfigs/answer) method. However, you have the option\nto specify your own preamble instead of using the default.\n\nFor instructions on how to specify the preamble in the answer method call, see\n[Specify a custom preamble](/generative-ai-app-builder/docs/answer#preamble-rest).\n\nFor example, you can use the preamble to do the following:\n\n- Specify words that the model can and can't use.\n\n- Specify topics to focus on or avoid.\n\n- Specify the style, tone, and format of the response.\n\nTailoring the preamble can significantly improve the quality of\nsummaries.\n\nThe preamble should have two parts:\n\n- **The task description** that describes the task you are asking the LLM to\n perform. See [Examples of task descriptions](#task-descriptions).\n\n- **Additional instructions** that the LLM should follow. See [Examples and\n tips for additional instructions](#additional-instructions).\n\nExamples of task descriptions\n-----------------------------\n\nHere are some examples of task descriptions. The scenario is that your\nemployees want answers from a data store that contains many company documents.\n\n### Example 1\n\nTask description to comprehensively cite sources: \n\n Given a user query and a list of sources, write a response that cites individual\n sources as comprehensively as possible.\n\n### Example 2\n\nTask description to understand the user and focus on helpfulness: \n\n You are an enterprise LLM summarization tool. Your task is to understand the\n true intent of a user question in the context of enterprise search and\n summarization, and provide a helpful answer to the user's question.\n\n### Example 3\n\nTask description to summarize a conversation between a customer and an\nassistant: \n\n Given the conversation between a customer and a helpful assistant with some\n search results, create a final answer for the assistant.\n\nExamples and tips for additional instructions\n---------------------------------------------\n\nThe additional instructions should capture your specific key requirements.\n\nThe following table gives examples of additional instructions that you might\nprovide after the task description, what kind of problems each example\naddresses, and why the preamble solves the problem.\n\nExamples of complete preambles\n------------------------------\n\nHere are some more examples of complete preambles, made up of the task\ndescription and the additional instructions.\n\n### Example 1\n\nRequest a concise, accurate, and relevant summary and present it in a\nuser-friendly format. \n\n You are an enterprise LLM summarization tool. Your task is to understand the\n true intent of a user question in the context of enterprise search and\n summarization, and provide a helpful answer to the user's question. Please keep\n summaries concise and focused, providing only the most essential information to\n address the user's query.\n\n Please also structure and format the summary by\n\n 1) prioritize most relevant and accurate information to user's question\n\n 2) highlight critical information\n\n 3) structure the response and adapt the formatting to be user friendly (e.g.,\n use bullet points for steps, numbered lists for rankings, tables for\n comparisons, code block for coding example, etc).\n\n### Example 2\n\nProvide a concise, friendly, and helpful final answer to a customer's query\nbased on a conversation. \n\n Given the conversation between a customer and a helpful assistant with some\n search results, create a final answer for the assistant.\n\n The answer should addresses the query accurately and concisely (less than 10\n sentences), while also being friendly and helpful. If the search results don't\n provide enough information to fully answer the question, suggest additional\n resources or steps the customer can take.\n\n### Example 3\n\nProvide comprehensive and understandable answers and cite given sources.\nPolitely decline to answer any political questions. \n\n Given a user query and a list of sources, write a response that cites individual\n sources as comprehensively as possible.\n\n The response should be suitable for a non-expert audience.\n\n For political questions, the response should be a polite refusal to answer the\n question.\n\nBest practices\n--------------\n\nThe following are some best practices for writing and tuning preambles:\n\n- **Iterative Refinement:** Experiment with different preamble\n variations and observe the impact on the answer quality.\n\n- **User Feedback:** Gather feedback from users to identify recurring issues\n and areas for improvement.\n\n- **Stay Updated:**The effectiveness of preamble tuning can vary\n depending on the model version and the nature of your documents. Continuously\n experiment and refine your approach to achieve optimal results.\n\n- **Thorough Evaluation:** Verifying modified preambles across all intended use\n cases helps identify and mitigate potential biases or unexpected behavior\n that may negatively impact summary quality in certain scenarios."]]