X-RaiseA Data Driven Prompt Engineering Approach Using Llms

Abstract

Introducing Xraise, an initiative aimed at transforming education through large language models (LLMs) and natural language processing (NLP). Xraise offers three innovative modules: 1. Interactive Chatbot with Dr. Israr Ahmad: This module allows users to engage with a virtual representation of Dr. Israr Ahmad, enabling them to ask questions, receive knowledgeable responses, and simulate real-life conversations in a specified domain of Dr. Israr Ahmad’s expertise. 2. Customizable Chatbot for PDF Data Extraction: This interactive tool empowers students and researchers to extract specific information from PDF documents. Users can tailor the extraction process to their needs, streamlining the process of gleaning valuable insights from research papers, articles, or other text-heavy PDFs. 3. Real-time PDF Summarization: Xraise tackles information overload by generating concise summaries of PDF documents. This feature allows users to quickly grasp the main points of a document, improving comprehension and accessibility, particularly for lengthy or complex materials. Xraise utilizes LangChain and Pinecone for scalability, ensuring efficient data handling and robust performance to accommodate a large user base. An integrated LLMs study within Xraise will assess the application’s effectiveness, highlighting its ability to enhance learning through AI-driven document interactions and fostering innovative educational solutions