Narae, came form Literary expression of ‘wings’ in Korean.
Narae is a project developed by the students of Chung-Ang University for an Open Source Software lecture. Our goal is to create a personal AI coaching mentor designed to guide users through daily growth, focusing on specialized areas of study. The project consists of a backend server and a frontend application, but our main focus is on backend.

• Back-End Server: Narae - Handles AI processing, user data management, and serves as the backbone of our service.
• Front-End App: Narae-Frontend - Provides the user interface where users interact directly with Narae.

Our development focuses on several key areas to create a effective mentoring service.

1. Motivation and Learning Setup:

   • Users can choose their field of development (e.g., backend, frontend).
   • We provide an effective learning plan considering the user's situation, goals, and intentions.
   • Goals are periodically reset to offer realistic mentoring.

2. Learning Guidance and Advice:

   • AI suggests tasks to foster expertise and guides on necessary resources.
   • For topics that are challenging, AI explains in depth and provides examples and exercises.

3. Review and Evaluation:

   • AI summarizes conversations to track learning progress.
   • Custom quizzes are generated daily to reinforce learned material.

4. Utilizing OpenAI Embeddings:

   • To provide deep knowledge in specific development areas, we integrate data using OpenAI's embedding technologies.

5. User Feedback Collection:

   • We actively collect user feedback post-service to enhance our offerings.

(Maybe) Voice Interaction Capability:

• Users can converse with AI anytime, anywhere via voice.
• Conversations are transcribed to text for backend processing and responses are returned as voice.
• It will be implemented if we have some extra time.

For the initial phase of Narae's development, we have streamlined our project scope by focusing exclusively on the field of development(e.g. backend, frontend) for mentoring. We believe that by narrowing our focus to this area, we can more efficiently implement and optimize our embedding technologies. We want to check if embedding gives more effective and specialized AI coach that can provide tailored guidance and resources to aspiring learners of development.

Our approach avoids costly fine-tuning; instead, we leverage technologies like Retrieval-Augmented Generation (RAG) when necessary to maintain affordability while ensuring high quality and responsiveness.