@Article{M-10131,
AUTHOR = {Patel, Chaitanya},
TITLE = {Hypothetical Retrieval-Augmented Generation (Hypothetical RAG): Advancing AI for Enhanced Contextual Understanding and Creative Problem-Solving},
JOURNAL = {Scientific Research Journal of Science, Engineering and Technology},
VOLUME = {2},
YEAR = {2024},
NUMBER = {1},
ARTICLE-NUMBER = {M-10131},
URL = {https://isrdo.org/journal/SRJSET/currentissue/hypothetical-retrieval-augmented-generation-hypothetical-rag-advancing-ai-for-enhanced-contextual-understanding-and-creative-problem-solving},
ISSN = {2584-0584},
ABSTRACT = {The advancement of artificial intelligence (AI) has introduced Retrieval-Augmented Generation (RAG), which improves response generation by incorporating retrieved documents from a corpus. Hypothetical Retrieval-Augmented Generation (Hypothetical RAG) expands this concept by integrating hypothetical or additional contextual information that might not be directly available in the retriever's corpus. This paper examines the significance of Hypothetical RAG, highlighting its potential to address ambiguity, facilitate exploratory analysis, and enhance creative content generation. Hypothetical RAG is handy in handling ambiguous or poorly defined queries by generating responses based on possible scenarios or interpretations. This capability makes it valuable for exploratory analysis, allowing researchers to consider various hypothetical situations and make informed decisions.Additionally, Hypothetical RAG supports creative writing by providing diverse ideas and content based on hypothetical contexts, fostering innovation and creativity. Its applications extend to scenario planning, which generates responses based on different future scenarios and complex decision-making, offering insights and suggestions based on hypothetical situations. Overall, Hypothetical RAG demonstrates transformative potential across various domains by enhancing AI systems' contextual understanding and problem-solving abilities.},
DOI = {}
}