Retrieval-Augmented Generation (RAG)
Description
Advanced techniques for implementing Retrieval-Augmented Generation to improve AI model accuracy and reduce hallucinations by grounding responses in authoritative sources.
Learning Objectives
Learners will understand the principles and implementation of RAG systems, learn how to build effective retrieval systems, integrate external knowledge bases with generative models, and implement grounding techniques to improve factual accuracy and reduce AI hallucinations.
Topics (7)
Understanding how text and multimodal content is converted to vector representations, similarity metrics, and efficient search algorithms for retrieving relevant information.
Methodologies for creating, organizing, and maintaining knowledge bases including document chunking, metadata management, and indexing optimization for retrieval efficiency.
Advanced techniques for improving retrieval quality including combining keyword and semantic search, implementing re-ranking algorithms, and multi-hop reasoning for complex queries.
Methods for ensuring generated content is properly grounded in source material including citation generation, fact verification, and consistency checking between retrieval and generation.
Implementation of RAG systems that can retrieve and ground responses using text, images, videos, and other multimedia content for comprehensive knowledge integration.
Comprehensive evaluation frameworks for RAG systems including retrieval accuracy metrics, generation quality assessment, end-to-end evaluation, and optimization strategies.
Foundation knowledge of RAG systems including the retrieval and generation components, how they work together, and the benefits of combining retrieval with generation for improved accuracy.