etrieval Augmented Generation: Concepts and Pipelines

Description

The source material outlines a workshop focused on Retrieval Augmented Generation (RAG), providing a foundational understanding of the core components and methodologies. It first explains Similarity and Distance Metrics, such as Cosine Similarity and Dot Product, which are used to quantify how alike data points are, and then details Embedding Models that convert input into contextual vectors to capture semantic structure. The document then describes Contrastive Learning techniques like Triplet loss and infoNCE, which train models to distinguish similar items from dissimilar ones, a crucial step for effective Query-Document Scoring using models like bi-encoders and cross-encoders. Finally, the material synthesizes these concepts into a practical guide for Semantic Search & Document Retrieval and demonstrates how these elements form a RAG Pipeline to enhance Large Language Model (LLM) performance by grounding answers in retrieved documents.