From Data to Dialogue: Personalizing Therapy with Large Language Models

Cookie-cutter advice is the #1 complaint users levy at mental-health chatbots. Personalization has to go deeper than “Hi [Name]!” Here’s how Atlas Mind turns raw intake data into hyper-relevant, empathetic conversations.

1. Structured Onboarding Survey

We gather goals, stressors, coping styles, and therapy preferences. Key answers become JSON profile objects that feed every message context.

2. Embedding User Memories

Important events are embedded and stored so the model can ask, “How did that presentation go?” weeks later—without storing full text by default.

3. Dynamic Prompt Assembly

Each user message triggers a prompt composer that injects relevant memories, user goals, and therapy phase before hitting the LLM.

4. Adaptive Technique Selection

The bot chooses interventions—mindfulness, CBT reframing, or solution-focused questions—based on historical efficacy.

5. Reinforcement via Feedback Signals

Thumbs-up responses boost certain intervention weights; thumbs-down dampen them.

6. Ethical Guardrails

Profiles remain on encrypted storage; users can delete or export data at any time to satisfy GDPR/CCPA.