Can Tho, Vietnam — AIPractixIn, a technology company focused on building practical AI systems, today announced the successful completion of a mentorship program supporting DUY – Tran Quoc, an Information Technology undergraduate student from Can Tho University in developing a graduation thesis on Retrieval-Augmented Generation (RAG) for medical chatbots.
The thesis, titled “Design and Implementation of a Symptom-Based Medical Chatbot Using Retrieval-Augmented Generation (RAG)”, was evaluated by the academic committee and awarded an “Very Good” grade, recognizing its technical rigor and practical relevance.
Addressing Reliability in Medical AI
Medical chatbots require a higher standard of accuracy and responsibility compared to general conversational AI systems. The mentored project focused on using RAG architecture to ensure that all chatbot responses were grounded in curated medical knowledge, reducing the risk of hallucination and unsupported claims.
The system was designed to analyze user-reported symptoms, retrieve relevant medical information, and generate structured, explainable responses intended for preliminary assessment only, with clear disclaimers that it does not replace professional medical diagnosis.
AIPractix’s Role in the Mentorship
Throughout the thesis period, AIPractix provided technical mentorship and system design guidance, including:
- Defining a robust RAG pipeline suitable for healthcare use cases
- Structuring medical knowledge around symptoms, conditions, and references
- Implementing safeguards to control chatbot behavior and limit responses to verified data
- Applying best practices for responsible and explainable AI in healthcare contexts
The project was aligned with a real-world use case from DeepMec, an AI-assisted symptom-checking platform under development by AIPractix, allowing the student to work on a problem grounded in production-oriented requirements.
Academic and Practical Impact
The evaluation committee highlighted the project’s:
- Clear system architecture
- Appropriate application of RAG in a medical domain
- Strong connection between academic research and real-world deployment scenarios
The student demonstrated solid understanding of retrieval-based AI systems, conversational AI design, and the importance of safety and control in medical applications.
Commitment to Education and Practical AI
AIPractix continues to invest in mentoring and educational collaboration, supporting students and researchers in applying AI to meaningful, real-world problems.
The company’s mentorship and research focus areas include:
- Medical AI and symptom-based decision support
- Retrieval-Augmented Generation (RAG)
- Conversational AI and chatbots
- Computer vision and multimodal systems
- AI platforms and production-ready APIs
AIPractix believes that long-term innovation in AI depends on practical experience, responsible design, and strong collaboration between industry and academia.
About AIPractix
AIPractix is a technology company dedicated to building practical, production-ready AI systems that solve real-world problems across healthcare, education, and commerce.
AIPractix – Where Practical AI Powers Real Life
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