Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/171966
Title: ChatGPT robotic process automation and AI chatbots for education
Authors: Chua, Jeremy Wen Yang
Keywords: Engineering::Computer science and engineering
Issue Date: 2023
Publisher: Nanyang Technological University
Source: Chua, J. W. Y. (2023). ChatGPT robotic process automation and AI chatbots for education. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/171966
Project: SCSE22-0712 
Abstract: This study delved into the development of a novel educational chatbot application known as Edu-AI. The overarching goal was twofold: first, to optimize prompt engineering by selectively utilizing state-of-the-art Language Models (LLMs) to generate high-quality prompts for users; and second, to provide accurate responses to user inquiries. Multiple techniques, including introspection and fact-checking, were implemented to ensure response precision. During development, it became evident that inherent language comprehension differences existed between LLMs and humans. However, meticulously crafted prompts could mitigate this limitation and empowered LLMs to produce more refined responses. The Edu-AI application was founded on an innovative recursive GPT prompt engineering approach, which involved the LLM iteratively evaluating and ranking the prompts it generated. This method was seamlessly incorporated into Edu-AI to amplify prompt generation, allowing users to select superior options and obtain correspondingly improved responses. Additionally, Edu-AI employed fact-checking techniques inspired by prompt pattern concepts. This directed the LLM to furnish corroborating information for its generated responses, thereby upholding accuracy and credibility. The developmental process faced substantial challenges, primarily stemming from the relative nascency of LLMs and the limited exploration of recursive prompting techniques up to that point. The consequent scarcity of comprehensive resources introduced further complexity. However, the endeavor provided valuable insights into prompt optimization and fact-checking methods for advancing chatbot quality.
URI: https://hdl.handle.net/10356/171966
Schools: School of Computer Science and Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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