Problem Statement Selector

Inspiration

Choosing a problem statement is often confusing when there are too many options. I wanted to create something simple that guides participants to the right choice without wasting time.

What it does

Problem Statement Selector is an AI-powered app that suggests the most suitable problem statement based on user preferences such as category, technology, difficulty, and innovation level.

How we built it

I built the app using AWS PartyRock, which runs on Bedrock generative AI models. The dataset of problem statements was added as context, and the app uses prompts to filter and suggest the best matches.

Challenges we ran into

Figuring out how to organize the dataset for AI to understand. Keeping the app simple but effective. Making the AI suggestions accurate while staying within PartyRock’s limits.

Accomplishments that we're proud of

Successfully built a working AI app without writing complex backend code. Created a clear, simple solution that anyone can use.

What we learned

How to use AWS PartyRock for quick AI app building. The importance of clear prompts and structured data for AI accuracy. How to present an idea simply and make it easy for others to understand.

What's next for Problem Statement Selector

Add more filtering options such as team size or time duration. Allow uploading of custom datasets for different hackathons. Improve the AI’s ranking ability to suggest not just one but the top three best matches.