Unlocking the Marketing and Sales Potential: Bridging Generative AI with Domain-Specific Data using Retrieval Augmented Generation Architecture

Visieu Lac () and Damian Leschik ()
Additional contact information
Visieu Lac: IU Internationale Hochschule
Damian Leschik: IU Internationale Hochschule

Chapter Kapitel 15 in Generative Künstliche Intelligenz in Marketing und Sales, 2024, pp 207-219 from Springer

Abstract: Abstract The landscape of artificial intelligence (AI) has undergone a paradigm shift due to the transformative impact of Large Language Models (LLMs), facilitated by their capacity to generate text closely simulating human language. Amidst the notable advancements of LLMs across diverse applications, these progressions underscore significant considerations pertaining to security and privacy of data. This paper addresses the security and privacy data concerns associated with generative AI by employing a retrieval augmented generation (RAG) architecture tailored to domain-specific data. The study showcases the efficacy of the RAG architecture using domain-specific data from marketing and sales domains. The results reveal remarkable suggestions for marketing strategies based on prior data analysis steps. While the outcomes were impressive, we noticed intermittent variations in result presentations. Consequently, we propose prioritizing the aggregation of results to attain a comprehensive answer. Notably, we can show that RAG architecture using domain-specific data can be used without compromising confidentiality and privacy.

Keywords: LLM; Deep Learning; AI; Domain-Specific Data; GPTs; Retrieval Augmented Generation (RAG); Marketing and Sales (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-658-45132-5_15

Ordering information: This item can be ordered from
http://www.springer.com/9783658451325

DOI: 10.1007/978-3-658-45132-5_15

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().