ZavoBot uses InstructGPT alignment architecture to provide safe, accurate medical responses that comply with regulatory standards.
SFT (Supervised Fine-Tuning)
Role-based System Instructions
Responses adapt automatically for patients with empathetic plain language and for doctors with academic clinical language.
Reward Model
Safety Tier Resolver
Response caution is adjusted according to medical concern classification and AI response variation limits.
PPO / RLHF
Audit Log and Refinement
User feedback and clinical evaluations by doctors are analyzed to refine instructions regularly.
Helpful
Role-aware Context Inclusion
Relevant Zavora medical features are recommended contextually according to the user concern.
Honest
Hallucination Guard
False diagnosis risk is reduced through clear limitations and medical fallback responses.
Harmless
Data Protection and Health Regulation Compliance
Patient medical data privacy is protected according to formal healthcare operating standards.
KL Penalty
Response Variation Limits
Response variation is limited for serious concerns so clinical information remains accurate and reliable.
Alignment Tradeoff
Safety and Completeness Balance
The system balances patient safety protections with the completeness of AI information.
