Extract the main issues and sentiments from the customer feedback on our telecom services.
Focus on comments related to service disruptions, billing issues, and customer support interactions.
Please format the output into a list with each issue/sentiment in a sentence, separated by semicolon.
Input: CUSTOMER_FEEDBACK
Classify the extracted issues into categories such as service reliability, pricing concerns, customer support quality, and others.
Please organize the output into JSON format with each issue as the key, and category as the value.
Input: TASK_1_RESPONSE
Generate detailed recommendations for each category of issues identified from the feedback.
Suggest specific actions to address service reliability, improving customer support, and adjusting pricing models, if necessary.
Please organize the output into a JSON format with each category as the key, and recommendation as the value.
Input: TASK_2_RESPONSE
Analyze the sales data to identify the number of sales of each record.
Please organize the output into a JSON format with each record as the key, and sales as the value.
Input: STORE_SALES_DATA
输出应该会包含每张唱片的销售数量，采用 JSON 格式。
任务 1b：分析在线播放数据
Analyze the streaming data to provide a the number of streams for each album.
Please organize the output into a JSON format with each album as the key, and streams as the value.
Input: STREAMING_DATA
Recommend a stocklist of about 20 records based on the most sold and most streamed records.
Roughly three quarters of the stock list should be based on record sales, and the rest on streaming.
Input: TASK_1A_RESPONSE and TASK_1B_RESPONSE
[[["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)：2025-09-04。"],[],[],null,["For complex tasks that require multiple instructions or steps, you can improve the model's\nresponses by breaking your prompts into subtasks. Smaller prompts can help you improve\ncontrollability, debugging, and accuracy.\n\nThere are two ways to break down complex prompts and ingest them into a model:\n\n- **Chain prompts:** split a task into subtasks and run the subtaks sequentially.\n- **Aggregate responses:** split a task into subtasks and run the subtasks in parallel.\n\nChain prompts\n\nFor complex tasks that involve multiple *sequential* steps, make each step a prompt and\nchain the prompts together in a sequence. In this sequential chain of prompts, the output of one\nprompt in the sequence becomes the input of the next prompt. The output of the last prompt in the\nsequence is the final output.\n\nExample\n\nFor example, suppose you run a telecommunications business and want to use a model to help you\nanalyze customer feedback to identify common customer issues, classify issues into categories, and\ngenerate solutions for categories of issues.\n\nTask 1: identify customer issues\n\nThe first task you want the model to complete is extracting meaningful data from raw customer\nfeedback. A prompt that achieves this task might be similar to the following, where\n\u003cvar translate=\"no\"\u003eCUSTOMER_FEEDBACK\u003c/var\u003e is a file that contains the customer feedback:\n\n| Extract data |\n|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| **Prompt:** ``` Extract the main issues and sentiments from the customer feedback on our telecom services. Focus on comments related to service disruptions, billing issues, and customer support interactions. Please format the output into a list with each issue/sentiment in a sentence, separated by semicolon. Input: CUSTOMER_FEEDBACK ``` |\n\nWe would expect the model's response to contain a list of extracted issues and sentiment from the\ncustomer feedback.\n\nTask 2: classify issues into categories\n\nNext, you want to prompt the model to classify the data into categories so that you can\nunderstand the types of issues customers face, using the response from the previous task. A prompt\nthat achieves this task might look similar to the following, where\n\u003cvar translate=\"no\"\u003eTASK_1_RESPONSE\u003c/var\u003e is the response from the previous task:\n\n| Classify data |\n|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| **Prompt:** ``` Classify the extracted issues into categories such as service reliability, pricing concerns, customer support quality, and others. Please organize the output into JSON format with each issue as the key, and category as the value. Input: TASK_1_RESPONSE ``` |\n\nWe would expect the model's response to contain categorized issues.\n\nTask 3: generate solutions\n\nNow, you want to prompt the model to generate actionable recommendations based on the\ncategorized issues to improve customer satisfaction, using the response from the previous task. A\nprompt that achieves this might look similar to the following, where\n\u003cvar translate=\"no\"\u003eTASK_2_RESPONSE\u003c/var\u003e is the response from the previous task:\n\n| Generate suggestions |\n|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| **Prompt:** ``` Generate detailed recommendations for each category of issues identified from the feedback. Suggest specific actions to address service reliability, improving customer support, and adjusting pricing models, if necessary. Please organize the output into a JSON format with each category as the key, and recommendation as the value. Input: TASK_2_RESPONSE ``` |\n\nWe would expect the model's response to contain recommendations for each category, aimed at\nimproving customer experience and service quality, which satifies our overall objective.\n\nAggregate responses\n\nIn cases where you have complex tasks but you don't need to perform the tasks in a specific\norder, you can run parallel prompts and aggregate the model's responses.\n\nExample\n\nFor example, suppose you own a record store and want to use a model to help you decide which\nrecords to stock based on music streaming trends and your store's sales data.\n\nTask 1: analyze data\n\nThe first thing you need to do is analyze the two datasets, streaming data and sales data. You\ncan run the prompts to complete these tasks in parallel. Prompts that achieve these tasks might be\nsimilar to the following, where \u003cvar translate=\"no\"\u003eSTORE_SALES_DATA\u003c/var\u003e is a file that contains\nthe sales data and \u003cvar translate=\"no\"\u003eSTREAMING_DATA\u003c/var\u003e is a file that contains the streaming\ndata:\n\n| Task 1a: analyze sales data |\n|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| **Prompt:** ``` Analyze the sales data to identify the number of sales of each record. Please organize the output into a JSON format with each record as the key, and sales as the value. Input: STORE_SALES_DATA ``` |\n\nWe would expect the output to contain the number of sales for each record, formatted in JSON.\n\n| Task 1b: analyze streaming data |\n|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| **Prompt:** ``` Analyze the streaming data to provide a the number of streams for each album. Please organize the output into a JSON format with each album as the key, and streams as the value. Input: STREAMING_DATA ``` |\n\nWe would expect the output to contain the number of streams for each album, formatted in JSON.\n\nTask 2: aggregate data\n\nNow you can aggregate the data from both datasets to help you plan your purchasing decisions. To\naggregate the data, include the output from both tasks as the input. A prompt that achieves this\nmight look similar to the following, where \u003cvar translate=\"no\"\u003eTASK_1A_RESPONSE\u003c/var\u003e and\n\u003cvar translate=\"no\"\u003eTASK_1B_RESPONSE\u003c/var\u003e are the responses from the previous tasks:\n\n| Aggregate sales and streaming data |\n|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| **Prompt:** ``` Recommend a stocklist of about 20 records based on the most sold and most streamed records. Roughly three quarters of the stock list should be based on record sales, and the rest on streaming. Input: TASK_1A_RESPONSE and TASK_1B_RESPONSE ``` |\n\nWe would expect the output to contain a suggested stocklist of about 20 records, based on record\nsales and streams, with more favor given to records with proven sales history than to those with\nmore streaming popularity.\n\nWhat's next\n\n- Explore examples of prompts in the [Prompt gallery](/vertex-ai/generative-ai/docs/prompt-gallery)."]]
