Evaluation of Semantic Search and its Role in Retrieved-Augmented-Generation (RAG) for Arabic Language

The abundance of information has driven the development of semantic search technologies that surpass traditional keyword-based search engines by understanding the context and intent of user queries through natural language processing (NLP) and machine learning [1]. Unlike conventional search methods that focus on matching keywords, semantic search interprets the meaning and relationships between words, aiming to mimic human understanding. This advancement enhances user experience across various applications, including web search engines, knowledge discovery, and personalized content recommendation systems, and most recently Retriever-Augmented Generation (RAG) [2]. RAG represents an innovative approach at the crossroads of information retrieval and natural language generation, leveraging the strengths of both fields to refine Artificial Intelligence (AI) based systems ability to comprehend and generate human-like text [3, 4]. By combining a sophisticated retrieval mechanism with a powerful generation model, RAG systems can produce detailed, contextually relevant responses that significantly improve standalone language models limitations in terms of precision and human-like generation.