Dublin, CA, USA – 94568
Research Article
World Journal of Advanced Research and Reviews, 2024, 24(03), 879-889
Received on 29 October 2024; revised on 09 December 2024; accepted on 11 December 2024
This study explores the integration of DevOps methodologies with Large Language Model Operations (LLMOps) to drive intelligent automation and innovation in e-commerce. It is our goal to forecast and examine consumer faith in online marketplaces by making use of GenAI and the neural computation powers of LLMs. The strategy's core goal is to improve confidence by means of a two-pronged approach: first, predictive modeling with LLMs; and second, causal analysis with QCA. In order to determine the degree of trust based on reviews, LLMs examine fundamental aspects of online shopping, such as product quality, customer service, refund policies, and delivery speed. At the same time, customer journey stages including product selection, delivery, and post-purchase assistance are uncovered by QCA as causal linkages between trust. The integration of LLMOps within a DevOps framework ensures efficient deployment and maintenance of AI models, fostering seamless innovation and operational agility. This hybrid approach offers e-commerce platforms actionable insights into enhancing customer trust while setting a benchmark for intelligent automation in the industry.
DevOps; Large Language Model Operations (LLMOps); GenAI; E-commerce; Intelligent Automation
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Rahul Kalva. Integrating DevOps and Large Language Model Operations (LLMOps) for GenAI-Enabled E-commerce Innovations A Pathway to Intelligent Automation. World Journal of Advanced Research and Reviews, 2024, 24(3), 879-889. Article DOI: https://doi.org/10.30574/wjarr.2024.24.3.3725