Summary:

Developed a virtual assistant for an educational institution that uses Retrieval-Augmented Generation (RAG) to provide accurate responses to student inquiries, resulting in a 30% reduction in the workload of administrative staff.

Problem:

Students frequently required assistance regarding course details, academic regulations, and administrative processes. The traditional approach relied heavily on staff to answer common questions, leading to inefficiencies and delayed responses that negatively impacted student experience.

Solution:

To resolve these issues, we implemented the following technology stack:

RAG: Employed to access a variety of data sources, including course catalogs, academic calendars, and frequently asked questions (FAQs) from previous queries. LangChain: Integrated the processing flow to manage the retrieval of relevant information and provide context-aware responses to student inquiries. Knowledge Base API: Customized the knowledge base by gathering data from internal documents and external educational resources to ensure comprehensive coverage of student queries. This virtual assistant enabled students to receive instant, accurate answers to their questions without needing to wait for human intervention.

Results:

The implementation led to a 30% reduction in administrative workload, enabling staff to focus on more strategic tasks. Student satisfaction scores improved significantly due to faster access to information, with over 85% of users reporting a positive experience with the virtual assistant.