假设我是一家律师事务所，我有这个表（基本结构）
*用户：姓名、邮箱、电话等。
*员工：种类、姓名、电子邮件、电话等。
*案件：案件名称、案件编号、当事人姓名、指派的律师、条目、上次更新、状态、open_date、close_date
*tasks：case_id、employee_assigned、employee_assigner、statusdue_at
*通信：date，user_id，employee_id，text，kind，duration
*cases_assignations：employee_id，case_id
假设现在我想用chatGPT或其他解决方案训练一个模型，例如，如果员工类型：
**Input**: I would like to know the tasks assigned to John that are days due
**output**: John has 3 tasks that are due, these are: Task 1, task 2 ,task 3
**Input**: I would like to how many cases are open
**output**: There are 8 cases open right now
**Input**: I would like to how when did john communicate last time with client Elena
**output**: John communicate with Elena on october 8 at 6 am on phone and it last 5 minutes
**Input**: I would like to how the cases that John as opened and last update is more than 5 days before
**output**: John has 8 cases open that are 5 days before, these are: case 1 ,case 2, case 3, case 4等等……有意义吗？我想知道使这成为可能的策略
1条答案
按热度按时间mf98qq941#
创建一个简单的应用程序来读取问题，然后在后台通过一些编程语言从数据库表中获取答案并返回答案，这不是更好吗？这不需要任何机器学习或人工智能解决方案。当你没有足够的参数来解决问题时，需要人工智能解决方案，所以你训练一个模型来学习自己的参数。在这种情况下，你已经有数据存在于表中，可以通过SQL或一些编程语言查询。