RAG UX/UI enhancment

Requirements

• M.Sc. in Machine Learning, Data Science, Computer Science, Mathematics, Telecommunications, or similar
• Knowledge of Python and React
• Software development skills
• Keen interest for UX/UI development
• Basic knowledge LLM, RAG, knowledge graphs

Description

This thesis/internship focuses on improving the user experience and user interface of an existing Retrieval Augmented Generation (RAG) application. RAG systems combine information retrieval techniques with large language models to generate answers grounded in external knowledge sources; It is important that users can clearly and intuitively interact with the application. The main objective of the work is to analyze the current frontend, identify usability limitations, and implement UX/UI improvements that make the system easier to use, more transparent, and more effective. The activity may include refining the chat interface, improving the presentation of retrieved sources, enhancing document previews, adding feedback mechanisms, and designing clearer ways to display citations, metadata, retrieval results, or knowledge graph-related information. The candidate will work mainly on frontend development using React, while also gaining practical exposure to the architecture of RAG-based applications and their integration with Python-based backend services. This is a practical and implementation-oriented thesis/internship, suitable for students interested in applied AI, human-computer interaction, frontend development, and the usability of LLM-based systems. The expected outcome is a refined frontend prototype that improves the interaction quality, usability, and explainability of the existing RAG application.

Contacts

• Luca Bruno
• Xileny Portocarrero
• Lorenzo Bongiovanni