Improving an Employee Stock Ownership Plan Communication Strategy with GenAI: Development of a GenAI Chatbot for an Employee Stock Ownership Plan in a major aerospace company
Year: 2025
Type: dissertation
Abstract: This thesis presents the development of a Retrieval-Augmented Generation (RAG) chatbot to support internal communication of the Employee Stock Ownership Plan (ESOP) at a major aerospace company. ESOPs often involve complex documentation and regulations, leading to difficulties in understanding and low employee engagement. To address this, a Generative AI chatbot was built using Google Vertex AI, LangChain, and Gemini large language models. The methodology included cleaning and analysing over 100 ESOP PDF documents, splitting them into chunks using optimal configurations, embedding them into semantic vectors, and testing different retrievers and generative models. Evaluation was conducted using the RetrievalAugmented Generation Assessment (RAGAS) framework across three metrics: faithfulness, answer relevancy, and noise sensitivity. The best-performing configuration combined hybrid retrieval with the text-embedding-005 model for embedding and gemini-2.0-flash-001 for generation. In addition to quantitative metrics, a qualitative user study with ten employees was conducted to assess usability and perceived helpfulness. Results demonstrate the chatbot’s potential to enhance information accessibility, reduce dependence on HR teams, and improve overall user experience.
Author: Rocha, Raquel Alexandra Ascensão
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7113305601