Problem Statement

Mental health support is often inaccessible due to cost, stigma, and availability. Many existing chatbots provide generic responses without understanding emotional context or providing evidence-based support.

Solution

Developed a RAG-based chatbot that retrieves information from curated mental health resources while using sentiment analysis to adapt its conversational tone. The system provides empathetic responses grounded in established psychological frameworks.

Key Features

• ▸Sentiment-aware response generation
• ▸RAG retrieval from curated psychological resources
• ▸Empathetic conversational design
• ▸Crisis detection and resource referral
• ▸Session context maintenance for coherent multi-turn conversations
• ▸Evidence-based coping strategy recommendations

Challenges

• ⚡Ensuring safe and responsible AI responses for sensitive topics
• ⚡Balancing empathy with accuracy in mental health information
• ⚡Implementing crisis detection with minimal false negatives
• ⚡Maintaining conversation coherence over long sessions

Results & Metrics

Lessons Learned

• 💡Responsible AI design is paramount for sensitive domains
• 💡User feedback loops are essential for improving empathetic response quality
• 💡Domain-specific fine-tuning of retrieval significantly impacts response relevance