deepeval

To evaluate LLM performance for hallucination and toxicity, you can test models against metrics like answer relevancy and bias. This process identifies potential risks and ensures AI systems meet required performance standards before deployment.

Checking AI compliance with EU AI Act Article 15 involves evaluating AI systems against specific regulatory requirements. This assessment ensures your models implement necessary controls and monitoring for performance risk mitigation.

Yes, you can integrate LLM evaluation into a CI/CD pipeline for continuous monitoring. This allows development teams to automatically assess model performance, hallucination, and regulatory compliance during the software development lifecycle.

The best way to assess bias in an AI chatbot is to run targeted performance testing that evaluates model responses for toxicity and answer relevancy. This identifies unbiased and accurate interactions before customer service deployment.

Yes, you need the DeepEval framework to run comprehensive AI compliance and risk assessments. It provides the necessary testing and reporting infrastructure to evaluate LLMs against performance metrics and regulatory standards.