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

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.