Exploring AI Text Generation, Retrieval-Augmented Generation, and Detection Technologies: a Comprehensive Overview
Fnu Neha, Deepshikha Bhati, Deepak Kumar Shukla, Angela Guercio, Ben Ward · 2025
The rapid development of Artificial Intelligence (AI) has led to the creation of powerful text generation models, such as large language models (LLMs), which are widely used for di-verse applications. However, concerns surrounding AI-generated content, including issues of originality, bias, misinformation, and accountability, have become increasingly prominent. This paper offers a comprehensive overview of AI text generators (AITGs), focusing on their evolution, capabilities, and ethical implications. This paper also introduces Retrieval-Augmented Generation (RAG), a recent approach that improves the contextual relevance and accuracy of text generation by integrating dynamic infor-mation retrieval. RAG addresses key limitations of traditional models, including their reliance on static knowledge and potential inaccuracies in handling real-world data. Additionally, the paper reviews detection tools that help differentiate AI-generated text from human-written content and discusses the ethical challenges these technologies pose. The paper explores future directions for improving detection accuracy, supporting ethical AI development, and increasing accessibility. The paper contributes to a more responsible and reliable use of AI in content creation through these discussions.