A Collaborative Starting Point
I was so excited about this opportunity that I began brainstorming, but I firmly believe the most impactful solutions are born from partnership. My first step would be to "Collaborate with stakeholders to gather and define requirements." By analyzing user needs, we can co-design powerful low-code AI solutions and deploy them in a fraction of the time it would take to build from scratch.
Ideas for Student & Faculty
Enhancing the academic experience.
Ideas for Operations & Career
Driving university-wide innovation.
Ideas for Campus Operations
Improving efficiency and accessibility.
My Approach to Effective Prompting
Best practices for reliable AI agents.
Extending the Platform: The Pro-Code Bridge
How my developer skills can add custom power.
Brainstorming Ideas: Student & Faculty Success
Use Case: Campus FAQs & Virtual Teaching Assistants
A Potential Idea: Using NebulaONE's Agent Builder, we could create a unified "OwlBot." By leveraging the platform's Knowledge Connectors, we could feed it the student handbook for general questions and allow faculty to connect course-specific materials for private, class-specific support.
Use Case: Admissions & Enrollment Management
A Potential Idea: An "Admissions Inquiry Analyzer" agent could be built to read and categorize prospective student emails. By engineering a precise System Prompt, we can instruct it to analyze sentiment, flagging urgent queries for human attention and providing the team with a dashboard of common questions.
Brainstorming Ideas: Operations & Career Services
Use Case: Operations Automation
A Potential Idea: A "Syllabus Compliance Checker" agent could be configured in NebulaONE. We would use a Knowledge Connector to link it to the official university policy documents. Its core prompt would instruct it to compare an uploaded syllabus against this trusted source and generate a detailed compliance report.
Use Case: Career Services & Alumni Engagement
A Potential Idea: Inspired by the UCLA case study, a "Career Prep Assistant" agent could help students adhere to resume standards by checking their uploaded resume against a knowledge base of FAU's approved formats and best practices. It could also automate administrative tasks for career coaches, freeing them for more impactful student guidance.
Brainstorming Ideas: Campus Operations
Use Case: IT Help Desk Automation
A Potential Idea: An "IT Help Desk Assistant" agent could be trained on FAU's IT knowledge base to provide instant answers to common tech support questions (e.g., "How do I connect to the campus WiFi?"). This would provide 24/7 support and free up IT staff to handle more complex hardware and software issues.
Use Case: New Hire & Student Onboarding
A Potential Idea: An "Onboarding Assistant" could guide new community members through their first weeks. It could answer questions like "Where do I get my Owl Card?" or "How do I set up my email?" and provide a personalized checklist of tasks, ensuring a smooth and welcoming start to their journey at FAU.
My Approach to Effective Prompting
To ensure every AI agent we build is effective and reliable, I would apply these best practices, which I mastered in the "ChatGPT Prompt Engineering for Developers" course from DeepLearning.AI.
Principle 1: Write Clear & Specific Instructions
- ➤Use Delimiters: Clearly separate instructions from context using ```, <>, or XML tags to avoid confusion.
- ➤Ask for Structured Output: Request a specific format (like a list, table, or summary) for predictable, easy-to-use results.
- ➤Check Conditions: Instruct the model to verify assumptions before executing a task.
- ➤Few-shot Prompting: Provide successful examples to guide the model's performance on a new task.
Principle 2: Give the Model Time to Think
- ➤Specify Steps: Break down complex tasks into a sequence of steps for the model to follow.
- ➤Inner Monologue: Instruct the model to work out its own solution before rushing to a conclusion, improving reasoning.
Bonus: Reducing Hallucinations
- ➤This is the core of RAG. We instruct the model to first find relevant information from our trusted FAU knowledge base, and then answer the question based only on that information. This drastically reduces the risk of false statements.
Extending the Platform: My Pro-Code Bridge to Success
Ready for the "In-House, Developer-Driven AI Projects." When a challenge requires a solution beyond the scope of low-code, I believe my developer background would allow me to contribute in a more technical capacity
1. Build Custom Engines
Using Python and ML libraries to build novel AI models from scratch when needed.
2. Integrate with NebulaONE
Containerize the model with Docker and expose it as a secure API, making it a new "tool" in the low-code platform.
3. Document & Train
Create comprehensive technical guides and user-friendly training workshops to ensure successful adoption.
This dual capability ensures we can not only maximize NebulaONE's features but also extend its power infinitely to solve any challenge at FAU. I am extremely excited to make AI magic happen.