2nd Runner Up — SPU AI Prompt Mini Hackathon 2024

Competed in the SPU AI Prompt Mini Hackathon 2024 at Sripatum University. Despite the competition's focus on prompt engineering and LLM-based approaches, our team took a contrarian strategy — instead of relying on LLMs for semantic analysis, we built a logistic classification model that outperformed prompt-based solutions on the evaluation criteria.

The approach demonstrated that traditional ML methods with proper feature engineering can still beat LLM-driven solutions in structured classification tasks, especially when interpretability, speed, and cost efficiency matter more than generative flexibility. Our team placed 2nd Runner Up.