DeveloperWeek Management 2024 + AI DevSummit 2024 (+ DW Europe/LATAM 2024)

Venkata Karthik Penikalapati, Salesforce, Lead Member of Technical Staff

The integration of Retrieval-Augmented Generation (RAG) into Large Language Models (LLMs) represents a significant advancement in artificial intelligence, blending the generative prowess of LLMs with the precision of information retrieval to produce contextually relevant and accurate outputs. This talk aims to dissect the methodologies and challenges involved in measuring the accuracy of RAG-based LLM systems, a crucial aspect for their application across diverse domains including, but not limited to, search engines, conversational agents, and domain-specific question-answering systems. We will explore various evaluation metrics and benchmarks that gauge the performance of these systems, touching upon both the generative and retrieval components. Additionally, the presentation will delve into the challenges of defining and measuring "accuracy" in a field where relevance and truthfulness are paramount yet often subjective. By examining case studies and current best practices, this talk will provide insights into strategies for enhancing model performance, alongside a discussion on the ethical considerations of deploying highly accurate RAG systems in sensitive areas. The ultimate goal is to foster a deeper understanding of the state-of-the-art in RAG technology and inspire continuous innovation and responsibility in its application.