Alessandro Memmolo
Evaluating the impact of few-shot learning approaches on a cloud based agentic generative AI platform for Text to SQL.
Rel. Lia Morra. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Matematica, 2025
Abstract
Abstract: The evolution of Generative Artificial Intelligence (GenAI) has led to a growing demand for scalable and efficient platforms for managing and executing advanced models. This thesis explores the design and implementation of a cloud-based GenAI Platform, consisting of an Agentic system of Large Language Models, highlighting the architectural challenges, technologies used, and best practices proposed by IT consulting firm Target Reply to one of its clients. The purpose of the system will be to transform questions asked in natural language into formal queries in SQL language, in order to facilitate data analysis activities within business processes. This thesis will explore the approach that allows the transition from the specific technical vocabulary used by the client in their requests to the rigid schemas of SQL databases.
A key aspect of the research will be to validate the effectiveness of the system within a business environment, where there is a very low margin for error: it will propose several metrics to evaluate the accuracy of the results provided by the agent, and it will study the impact of the “Few-Shot Learning” strategy on the accuracy of the model
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