AI in healthcare needs to be reliable to avoid misleading diagnoses, poor research, and perpetuating bias in the public. To protect against this, Wisecube uses input/ output validators in Pythia. Validators validate LLM responses to ensure inputs meet quality standards and outputs are factually correct.
To implement robust input and output validation mechanisms, Pythia uses advanced input and output validators. Validators detect and prevent erroneous and unreliable LLM responses.
Pythia input/ output validators serve the following objectives:
To implement robust input and output validation mechanisms that enhance the reliability and safety of AI outputs using Pythia’s API
To minimize the risk of AI hallucinations and inaccuracies through proactive monitoring and validation.
To ensure compliance with industry standards and regulations by maintaining strict data quality and model output standards.