Essence

Retrieval-Augmented Generation (RAG) stands out as a critical approach to enhance Artificial Intelligence Generated Content (AIGC), helping overcome challenges related to maintaining current knowledge and handling data leakage. This survey thoroughly reviews RAG’s impact across various AIGC applications, shedding light on its potential to bring about more accurate and robust outcomes.

Key Points:

• RAG introduces information retrieval processes to AIGC, significantly improving results by accessing relevant data.
• The survey categorizes RAG foundations based on augmentation methodologies.
• Additionally, the paper addresses the engineering aspects of implementing RAG systems.

Significance: The integration of RAG into AIGC is highly pertinent, given the exponential rise of data and the shifting landscape of information access. This survey proffers a structured understanding for both researchers and practitioners, indicating RAG’s pivotal role in driving the evolution of AIGC. With its detailed classification and evaluation, the survey is a cornerstone for further innovations in the intelligent generation of content across multimodalities. Learn More