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Fundamentals of Retrieval Augmented Generation

Level:
intermediate
Duration:
30 minutes

Abstract

Retrieval Augmented Generation (RAG) has emerged in recent years as a popular technique at the crossroads of Information Retrieval and Natural Language Generation. It represents a promising new approach that combines the strengths of both retrieval-based systems and generative AI models, aiming to address the limitations of each, while enhancing their overall performance on document intelligence tasks. This talk will introduce the key frameworks, methodologies and advancements in RAG, exploring its ability to empower Large Language Models with a deeper comprehension of context, by leveraging pre-existing knowledge from external corpora. We will review the theoretical foundations, practical applications, and technical challenges associated with RAG, showcasing its potential to impact various fields, such as document summarization or database management. Through this talk, attendees will gain insights into the most relevant topics related to RAG, including token embedding, vector indexing and semantic similarity search.


The speaker

Catalin

Catalin

Catalin Hanga (PhD) is a Data Scientist and Machine Learning Engineer, currently working at the Open Innovation AI Lab of Iveco Group in Switzerland. Among his recent projects include developing a documentation management chatbot based on Retrieval Augmented Generation, as well as implementing an autonomous LLM agent using the ReAct framework. Prior to this, he briefly worked in a similar role for a startup in the insurtech industry. He has obtained a PhD in Mathematics from the University of York, UK, and holds a M.Sc. in Physics from the University of Bucharest. During his M.Sc. studies, he also worked as a researcher at CERN in Geneva, Switzerland, analyzing experimental data collected by the detectors of the Large Hadron Collider. He has previously given public presentations at academic events, as well as industry conferences.