This project aims to develop a custom Large Language Model (LLM) to assist business owners and families in crafting a "Corporate Charter." The LLM will guide users towards a unified vision, mission, values, and philanthropic goals.
Traditional Approach:
- Specialized advisors traditionally handle this process.
Proposed Solution:
- The LLM will replace the advisor role by:
- Employing probing questions to uncover stakeholders' or family members' underlying values and goals.
- Compiling these findings into a cohesive charter document for review and agreement.
- Allowing users to edit and refine the charter collaboratively.
Technical Approach:
- The LLM will leverage existing LLM APIs, fine-tuned on relevant business literature and regulations.
- Users will interact with the LLM through a progressive web app interface.
- AI/ML Components: Primarily Python
- Progressive Web App Interface: Typescript
- Programming Languages: Python, Typescript
- Libraries/Frameworks:
- PyTorch for deep learning
- Selenium for web scraping (potentially)
- Pandas for data manipulation
- Other AI/ML libraries as needed
- APIs: Existing LLM APIs for model interaction
- Tools:
- IDE for development
- Code interpreter
- Web browser for research
- Potentially DevOps tools for CI/CD (continuous integration and deployment)
- Computational Resources: Local AI/ML server with high computational power for training, development, and deployment of the LLM.
- Currently, charters are developed through consultations with family offices or specialized advisors.
- This project proposes automating and enhancing this process using an LLM.
Technical Implementation:
- The development will focus on Python and Typescript.
- Data science, machine learning libraries, and LLM APIs will be used to create the LLM's questioning pipeline and charter compilation functionalities.