Paper Reading : Enhancing Retrieval-Augmented Generation: A Study of Best Practices

​Details

​We will walk through the paper:

​Enhancing Retrieval-Augmented Generation: A Study of Best Practices

​[2501.07391v1] Enhancing Retrieval-Augmented Generation: A Study of Best Practices

​Abstract
Retrieval-Augmented Generation (RAG) systems have recently shown remarkable advancements by integrating retrieval mechanisms into language models, enhancing their ability to produce more accurate and contextually relevant responses. However, the influence of various components and configurations within RAG systems remains underexplored. A comprehensive understanding of these elements is essential for tailoring RAG systems to complex retrieval tasks and ensuring optimal performance across diverse applications. In this paper, we develop several advanced RAG system designs that incorporate query expansion, various novel retrieval strategies, and a novel Contrastive In-Context Learning RAG. Our study systematically investigates key factors, including language model size, prompt design, document chunk size, knowledge base size, retrieval stride, query expansion techniques, Contrastive In-Context Learning knowledge bases, multilingual knowledge bases, and Focus Mode retrieving relevant context at sentence-level. Through extensive experimentation, we provide a detailed analysis of how these factors influence response quality. Our findings offer actionable insights for developing RAG systems, striking a balance between contextual richness and retrieval-generation efficiency, thereby paving the way for more adaptable and high-performing RAG frameworks in diverse real-world scenarios. Our code and implementation details are publicly available.

​We are a group of applied AI practitioners and enthusiasts who have formed a collective learning community. Every Wednesday evening at PM PST, we hold our research paper reading seminar covering an AI topic. One member carefully explains the paper, making it more accessible to a broader audience. Then, we follow this reading with a more informal discussion and socializing.

​You are welcome to join this in person or over Zoom. SupportVectors is an AI training lab located in Fremont, CA, close to Tesla and easily accessible by road and BART. We follow the weekly sessions with snacks, soft drinks, and informal discussions.

​If you want to attend by Zoom, the Zoom registration link will be visible once you RSVP. Note that we have had to change and add security to the Zoom link to prevent Zoom bombing.

​
Speaker :
Asif Qamar

​LinkedIn: /asifqamar

​Technology Leader | AI/Data Scientist | Computer Scientist | Educator | Theoretical Particle Physicist
Technical Leadership

​Primarily interested in technical leadership positions that couple visionary leadership with a high‐octane, technical involvement in
applied AI/Machine learning. What distinguishes me is a technical leadership that brings together extensive, hands‐on technical, AI,
and architectural ability on the one hand and a capacity to bring together a very productive, creative, passionate, and happy team
across geographical boundaries.

​Track record of consistently delivering more than a dozen successful products of enduring value that I was instrumental in envisioning,
crafting the architecture of, doing the early R&D, prototyping, and then building together a dedicated, cohesive, and talented team
around to take the ideas to fruition, through significant projects. Without fail, the products that I have led the creation of are in extensive
deployment and healthy evolution after many years.

​Teaching & Mentoring
Over 20 years of leading, teaching, and mentoring engineers, through team-building around non-trivial projects, classes at universities, workshops, brown bags, and other informal gatherings. Currently, running off-work hours evening workshops in AI/Data-science/Machine-learning and Cloud computing.

​You are welcome to join this in person or over Zoom (https://us02web.zoom.us/meeting/register/tZUvf-uvrTwvHdP9B-vE03j3BapgRypn64CS). SupportVectors is an AI training lab located in Fremont, CA, close to Tesla and easily accessible by road and BART. We follow the weekly sessions with snacks, soft drinks, and informal discussions.