🔍 Inspiration
In an age where LLMs (Large Language Models) are becoming integral to decision-making, education, and productivity, there's a growing need to evaluate and compare multiple AI-generated responses efficiently. AI-MULTIVIEW was born from the desire to empower users—students, developers, and researchers—to analyze, rank, and understand responses from different AI models side-by-side to ensure the most accurate, context-relevant, and informative output is selected.
đź’ˇ What it does AI-MULTIVIEW allows users to:
Input a prompt or query and receive responses from multiple AI models (like OpenAI, Hugging Face, etc.).
Compare responses visually with highlighting and scoring mechanisms.
Use NLP-based evaluation to rank answers based on coherence, relevance, and factuality.
Export the query history and comparisons for future reference or academic use.
Offer a dashboard-style interface that supports real-time switching between models.
🛠️ How we built it
We used the following tech stack and methodologies:
Frontend: React.js with TailwindCSS for a responsive and minimal UI.
Backend: Python (FastAPI) to handle query distribution to different models.
AI Integration: Used APIs from OpenAI and Hugging Face to fetch model responses.
Evaluation Engine: Custom-built NLP scoring logic using cosine similarity, ROUGE, and sentence embeddings.
Export Module: Enabled PDF/CSV export using Python libraries like ReportLab and pandas.
⚠️ Challenges we ran into
Latency issues while fetching responses from different APIs simultaneously.
Standardizing output from different AI models to a common format for comparison.
Handling token limits and rate limits for each model during high-load scenarios.
Balancing performance and accuracy in the NLP evaluation engine.
🏆 Accomplishments that we're proud of
Successfully created a dynamic evaluation system that can provide real-time ranking of AI-generated content.
Built a user-centric, no-login-needed interface with history persistence and export capability.
Integrated multiple AI models and handled multi-response aggregation efficiently.
Developed a tool that's not just technical, but also educational and insightful for end users.
đź“š What we learned
Deeper understanding of evaluation metrics for NLP, such as BLEU, ROUGE, and BERTScore.
API management and concurrency control across multiple third-party AI platforms.
Importance of user experience in AI-based tools—clarity and speed matter as much as output quality.
Real-world edge cases in prompt engineering and how different models interpret input differently.
🚀 What's next for AI-MULTIVIEW
Add support for user-uploaded prompts in bulk for batch evaluations.
Introduce voice input and output analysis for accessibility.
Build a leaderboard or benchmark page showing top-performing models for various query types.
Integrate explainability tools like LIME or SHAP to show why a particular response ranked higher.
Expand to support multi-language queries and comparisons.
Log in or sign up for Devpost to join the conversation.