Retrieval-Augmented Generation (RAG) represents a paradigm shift in natural language processing (NLP), integrating large language models (LLMs) with dynamic information retrieval systems to produce responses that are both contextually enriched and factually grounded (Lewis et al. 2020). At its core, the RAG architecture couples a conventional generative model—one that
Generative AI relies on a specialised branch of machine learning (ML), namely deep learning (DL) algorithms, which employ neural networks to detect and exploit patterns embedded within data. By processing vast volumes of information, these algorithms are capable of synthesising existing knowledge and applying it creatively. As a result, generative
Generative artificial intelligence (generative AI or GenAI) tools represent an emerging branch of artificial intelligence (AI) algorithms (Tan et al. 2024, 168). In contrast to traditional AI models, which typically categorise data or make predictions based on learned patterns (Turchi et al. 2023, 35), generative AI extends beyond the mere
Gartner’s 2024 Technology Hype Cycle—which outlines the dynamics of expectations surrounding technological innovations across five distinct phases—indicates that generative AI has surpassed the peak of inflated expectations. Nevertheless, the associated hype remains persistent (see figure below), and the technology continues to hold the potential to become truly
The concept of generative artificial intelligence (GenAI) has long been employed in academic and technological discourse; however, it only began to garner widespread recognition in the mid-to-late 2010s, a period marked by pivotal breakthroughs in AI development that fundamentally transformed the landscape of its potential applications. Early in the decade,
