Retrieval-Augmented Generation (RAG) is probably one of the most popular implementations of LLMs that integrates retrieval and generation models, augmenting AI’s understanding of text and improving response accuracy through an information database.
This approach tackles the limitations of traditional generation models by fusing retrieval mechanisms, and enriching outputs with contextual depth and external knowledge.
Evaluating applications using LLMs, such as RAG, is pivotal for confidence and improvement, yet faces challenges like subjectiveness with respect to domain-specific suitability.
This presentation covers essential aspects to assess and optimize RAG performance.
