提示的五大支柱
超越简单的聊天界面，大型语言模型（LLMs）它们作为结构化数据处理的复杂引擎。本课程介绍了五个功能“基本单元”，使开发者能够将人工智能整合到专业工作流程中。
功能路线图
- 摘要生成：将大量文本（如产品评论）压缩为简洁摘要，同时保留关键信息。
- 推理分析：执行自动化分析，例如情感检测、情绪识别，或从文本中提取特定数据点（如品牌名称）并转换为JSON格式。
- 转换处理：将文本从一种形式转换为另一种形式——包括语言翻译、语气调整（例如正式转为非正式），以及数据格式转换（例如JSON转为HTML）。
- 内容扩展：将简短信号或要点扩展为长篇内容，例如个性化客户服务邮件。
- 聊天机器人：利用对话历史和预定义角色（系统、用户、助手）构建交互式、上下文感知的智能助手。
大型语言模型的无状态特性
关键概念： 与人类不同，大型语言模型是“无状态”的。除非您在每次API调用中手动传回完整的对话历史，否则它们不会记住之前的交互。这种累积的历史被称为 上下文。
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Question 1
A developer needs to extract the "Price" and "Discount Code" from 1,000 emails and save them to a database. Which approach and temperature setting are most appropriate?
Challenge: Tone Transformer
Apply the Transforming and Chatbot role concepts.
You are building a "Tone Transformer" for a corporate helpdesk. You need to convert a frustrated user's slang-heavy complaint into a "Formal" and "Polite" summary for a manager.
Task
Specify the role of the "System" message to ensure the model acts appropriately, and write the "User" prompt to translate: 'The app is buggy as heck and I want my money back ASAP!'
System Message:
User Prompt:
Expected Result:
"You are a professional executive assistant. Your task is to rephrase communication into a formal, corporate-appropriate tone."User Prompt:
"Translate the following text into a formal summary: 'The app is buggy as heck and I want my money back ASAP!'"Expected Result:
"The customer is reporting technical instability within the application and is requesting an immediate refund."