> For the complete documentation index, see [llms.txt](https://docs.maiagent.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.maiagent.ai/maiagent-user-guide/maiagent-user-guide-en/application/km/meeting.md).
# Meeting Requirement Management
As an enterprise employee, you frequently attend meetings and often encounter issues with meeting records being scattered and lacking centralized management, making it difficult to quickly find and track past decisions and requirement changes. Now, you can leverage MaiAgent to build a dedicated internal enterprise AI meeting management system to drive work forward more effectively.
## Use Case: Build an AI Meeting Management Assistant for a Software Company
Suppose you are a project manager at a software company. Facing frequent changes in internal and external requirements, you may often find it difficult to reach consensus quickly when communicating with stakeholders such as clients or managers, or key decision-makers repeatedly overturn previous decisions, making it hard to quickly verify the basis for decisions.
For example, your company has been commissioned by Daikin Co., Ltd. to develop a customized quotation management system. You have already completed the development of the version control feature based on the initial requirements submitted by the client's sales department staff. However, in a subsequent meeting, the client's sales director requested simplification of the feature, and in the most recent meeting, the client's sales manager raised even more complex feature requirements for the same function.
In the past, when facing such multiple and contradictory requirement changes, you might have spent a significant amount of time manually searching through scattered meeting record files on your computer to find the decision basis — both time-consuming and labor-intensive.
Now, by using MaiAgent to build an internal meeting management assistant, you can quickly access complete and up-to-date meeting history information, improving information transparency and communication efficiency, and effectively driving project progress!
## Workflow
### 1. Create an AI Meeting Management Assistant
Enter the AI assistant name.
Select RAG and language model.
When selecting the response mode, choose "Standard Mode" if you have no special requirements, which is suitable for this use case. Refer to the following role instructions:
```
# Role
You are the company's internal meeting management assistant.
# Output Format
Use the example format below to reply with the three most relevant meeting resolutions.
Your question is related to the following meeting resolutions:
1. Original content of the proposal
2. Original content of the proposal
3. Original content of the proposal
# Output Restrictions
- Reply in Traditional Chinese
- Do not answer questions outside the knowledge base scope
- Answer based on knowledge base data; if unable to answer, use the text within the below
Sorry, the knowledge base does not currently contain the information you requested.
```
### 2. Upload Knowledge Base
Upload historical meeting record files (the files below are examples designed for this use case).
### 3. Deploy the AI Assistant
Now you can ask questions directly through the AI assistant's chat interface to quickly understand the change history of quotation system version control feature requirements, identify who proposed each requirement, and access the source data files for further verification and confirmation.
Now you can ask questions directly through the AI assistant's chat interface to quickly understand the change history of the quotation system version control feature requirements, identify who proposed each requirement, and the AI assistant also provides relevant source data files for further verification and confirmation.
This significantly reduces your time spent searching for information, allowing you to focus on subsequent stakeholder management and coordination, effectively driving project progress.
---
# Agent Instructions
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## Querying This Documentation
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```
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