After the faculty meeting on Wednesday, the biggest takeaways are:
- [scope ]need to prove that our solution/feature is interesting and valuable
- [technical feasibility] Uncertainty about whether the hardware can run the LLM well enough, and if the LLM can complete our tasks well enough.
So we
- Put together a user story that describes the scenario and pain points of users, and how our solution solves the problem and compares to other existing solutions. We defined our users to students managing multi-source academic writing, individuals with communication impairments needing typing assistance, and professionals with privacy concerns around intellectual property. These stories demonstrate how SPARK solves real problems and clearly differentiate our solution from existing alternatives like cloud-based AI assistants and manual workflow tools. We documented specific use cases including context-aware citation formatting, intelligent autocorrection that preserves meaning, and private workflow acceleration without data exposure risks.
- Working on deploying the most powerful model on Jetson Nano Orin, also waiting for more powerful devices that we can borrow.
Moving forward, we want to finalize our UI design with mockups of the touchscreen interface and physical button layouts, to make sure the flow supports our usability requirements. We’re also creating a very clear, step-by-step walkthrough of our product that shows the complete user experience from setup through daily use across different scenarios. This will serve as a way to keep us aligned later in development.
We are going to develop a testing plan that addresses both quantitative metrics like latency and acceptance rates, as well as the more qualitative parameters that faculty raised concerns about in our design presentation. Specifically, we need rigorous evaluation criteria for the correctness of grammar fixes, appropriateness of tone adjustments, and overall quality of generative content. Such as designing test cases with clear rubrics and planning what our user testing sessions would look like as well.
Challneges:
Time is our biggest blocker as we need to prove a lot that out lllm deployment is strong enough to accomplish what we want to do for our project.
Tatyana: User story development, testing plan framework
Sida: LLM deployment on Jetson,
Leonard: hardware research
