# Trust and verification K Pro is engineered with multiple layers of safeguards to ensure scientific rigor, transparency, and accountability. These measures work together to minimize errors while empowering researchers to critically evaluate AI-generated insights. ### Evidence Provenance and Traceability Every conclusion generated by K Pro is anchored in evidence from authoritative sources, including PubMed literature and validated biological knowledge bases. The platform maintains complete provenance for all outputs, allowing users to trace recommendations back to their original sources. Comprehensive logging captures every data source, model decision, and reasoning step, creating a fully auditable trail of the analysis process. ### Technical Safeguards Against Hallucinations K Pro implements several technical mechanisms to combat the hallucination risks inherent in large language models: * **Retrieval-Augmented Generation (RAG)**: A RAG system verifies the existence and relevance of cited PubMed articles, ensuring that literature references are genuine and pertinent to the scientific question * **Tool-based grounding**: K Pro's architecture relies heavily on specialized tools—including modality-specific AI models and data query systems—whose proper execution is continuously monitored * **Data-anchored analysis**: One of K Pro's core strengths is its foundation in real patient data. Analyses and visualizations are generated from actual queried datasets, making it impossible to fabricate results ### The Role of Human Expertise While K Pro incorporates robust verification mechanisms, we acknowledge that no AI system is infallible. Scientific oversight and expert review remain essential components of responsible research. K Pro is designed to augment—not replace—human expertise and decision-making. Like any scientific tool, its outputs require thoughtful interpretation and validation by qualified researchers.This multi-layered approach to trust and verification enables K Pro to maintain the highest standards of scientific integrity while providing researchers with transparent, accountable AI assistance. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.owkin.com/governance-and-security/model-governance/trust-and-verification.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.