A comprehensive LLM comparison platform enabling data scientists to evaluate and compare different language models efficiently.
The Problem
As LLMs proliferate, data scientists need efficient ways to evaluate and compare model performance across different metrics and scenarios.
The Solution
Built a Streamlit-based platform that allows side-by-side comparison of LLMs, with features for testing robustness, detecting hallucinations, and analyzing performance patterns.
Impact & Results
Reduced model evaluation time by 60% and improved decision-making accuracy in model selection for production deployments.
Key Learnings
Gained deep insights into LLM evaluation metrics, prompt engineering best practices, and the importance of systematic testing in AI development.