Design and Implementation of an AI-Driven Mental Health Chatbot: A Generative AI Model

This paper describes the development, design, and maturation of an AI-based mental health chatbot that allows a user to engage with more accessible, higher forms of mental health assistance. As developed as a retrieval-based system which leveraged predefined responses contained in a database and based upon input received from a user, it matured into a far more scalable system which included the generative AI capabilities that allowed users to achieve far more personalized, contextually relevant interactions. This deployment variant robustly leverages on the chaining framework provided by LangChain to enable chaining of prompt templates, utilizes the OllamaLLM model that uses the Llama3 language model, and fully exploits other NLP methods. Therefore, the rule-based system is migrated to a generative model that integrates both algorithms of machine learning for effective enhancement of both understanding and generating. The primary issues with this development were about the conversational context, empathetic and coherent response, and the scalability of the system. All these were achieved through advanced prompt engineering, a set of rigorous evaluation metrics like BLEU score, accuracy, precision, recall, and F1 score besides continuous user feedback for refining the performance of this model. This paper discusses methodologies to overcome the challenges and implications for the future of AI and mental health care with an emphasis on embedding technological advancements within empathetic human-computer interaction. The findings point out this chatbot is effective in providing emotional support and how AI is changing its role to improve the services of mental health.