1. Designing the Report Schema:
   Structuring a detailed yet flexible JSON schema for AI-generated mental health reports was tricky. We iterated multiple times to ensure it covered emotional, reflective, and progress-based dimensions without overwhelming the user.
2. Prompt Engineering:
   Getting the AI model to generate emotionally intelligent, human-like reports was a major hurdle. We refined our system prompts through experimentation to balance structure with empathy.
3. Data Aggregation for Reports:
   Combining user past conversations, previous reports, and current micro-exercises into a meaningful, context-aware report required building a robust aggregation and feeding mechanism.
4. Maintaining User Privacy:
   We had to ensure that all mental health data was stored securely and handled sensitively, following best practices for user confidentiality.
5. Context Aggregation:
   Merging previous user conversations, past reports, and the current exercise into a meaningful, context-aware prompt for AI was technically intensive.

How we overcame it:

• Tight schema design iterations with validations.
• Fine-tuned system prompts after analyzing multiple outputs.
• Layered AI context ingestion methods for better output.
• Encrypted sensitive user information and anonymized storage.