RAG Without the Lag: Interactive Debugging For RAG Pipelines

Quentin Romero is currently a Research Assistant at the University of Pittsburgh and will be joining character.ai as a Member of Technical Staff Intern in May 2025. His work focuses on the intersection of Human-Computer Interaction (HCI) and Artificial Intelligence, with projects like "RAGGY," a visual interactive tool for RAG pipelines, and "BizChat," a tool for small business owners to generate business plans using large-language models.

Quentin has a strong research background with publications in CSCW and an upcoming paper in VLDB. His work has been recognized with numerous awards, including 1st place in the Kuzneski Innovation Cup and "Most Technically Elegant Hack" at HackMIT.

Shreya Shankar is a final-year PhD student in EECS at UC Berkeley. Her research focuses on building reliable and efficient AI-powered data systems: specifically, developing query optimization algorithms that make AI-powered data analysis both accurate and affordable, as well as interfaces that help users specify their intent and trust the results. Her work, including DocETL (VLDB 2025) and DocWrangler (UIST 2025), has been deployed across multiple domains such as law, climate, and finance, and appears in top venues like VLDB, UIST, and SIGMOD. Outside of research, she co-teaches "AI Evals for Engineers," a popular online course for LLM evaluation and testing, which has enrolled 1,500+ participants from 500+ organizations to date.