Chatbot Interaction Personalization

Chatbot Interaction Personalization is a framework for tailoring large language model–based chatbot interactions to individual patients using retrieval augmented generation (RAG). Instead of retraining or fine-tuning the underlying model, the system ingests persona descriptions or curated conversation fragments and uses them as a retrieval layer that shapes each response, allowing the chatbot to adjust tone, content emphasis, and examples to match user needs. Clinicians or care teams define persona profiles and the system automatically tailors interaction style and information accordingly. The approach functions as a modular personalization agent that sits in front of existing health chatbots, scales across large patient populations, integrates with current telehealth and digital health platforms, and can be updated simply by modifying persona content without changes to the underlying model.

Background: 
Most health chatbots deliver one-size-fits-all information, often in inaccessible language and in a tone that may be confusing, stigmatizing, or misaligned with patient context. Existing personalization options often rely on scripted branching logic or specialized teams to fine-tune models, making them difficult to configure and maintain. Chatbot Interaction Personalization uses RAG as a lightweight personalization layer controlled through interpretable persona descriptions or conversation histories, enabling clinically appropriate, patient-specific communication without modifying the base model and improving relevance, comprehension, and patient engagement.

Applications: 

• Telehealth chatbots delivering condition-focused education and self-management support
• Behavior-change and lifestyle coaching tools (diet, exercise, medication adherence)
• Digital front doors for pediatric, geriatric, and neurodiverse patient populations
• Mental health and wellness chatbots requiring sensitive, tailored communication
• Patient engagement tools for insurers, health systems, and care management organizations

Advantages: 

• Delivers personalized content, tone, and reading level based on persona profiles 
• Avoids costly model retraining or fine-tuning
• Enables clinicians and non-technical staff to design and update personas using concise narrative descriptions
• Scales across diverse patient groups and use cases within a single chatbot platform