Overview: I led the creation of an AI-powered chatbot designed to assist students with inquiries about the “Professional Seminar 2” course. The project utilized advanced AI techniques, including Retrieval-Augmented Generation (RAG), to provide accurate and contextually relevant responses.
Key Contributions:
Natural Language Processing: Integrated pre-trained models like GPT-3 to enable the chatbot to understand and generate natural language responses, ensuring accurate and human-like interactions.
Efficient Data Retrieval: Leveraged Pinecone as a vector database to efficiently store and retrieve document embeddings, facilitating quick access to relevant course materials.
Seamless User Experience: Employed LangChain to manage the interaction flow, seamlessly connecting user queries with the appropriate document retrieval and response generation. Built a user-friendly interface using Streamlit for intuitive interaction.
Challenges Tackled:
Data Standardization: Developed a pipeline to automate the cleaning and embedding of course-related data, overcoming initial challenges in data preparation to enhance retrieval accuracy.
System Integration: Unified various components (LLM, Pinecone, LangChain, Streamlit) into a cohesive system, incorporating error handling and optimizations to boost performance and reliability.
Impact & Future Potential:
Improved Learning: The chatbot significantly enhanced students’ learning experiences by providing instant, accurate answers to course-related queries.
Increased Efficiency: Streamlined the process of finding relevant information, reducing the time and effort required for students to access key course materials.
Scalability: Designed the system with future expansion in mind, laying the groundwork for additional features such as multilingual support and voice interaction capabilities.