New method for testing AI using real work-flows (github.com)

A new evaluation framework named LLM INQUISITOR has been unveiled, aimed at assessing how AI systems perform in real-world workflows rather than in controlled testing environments. This innovative methodology addresses a critical gap in AI evaluation—it reveals issues like instability, unpredictability, and contradictions during actual use cases such as coding sessions and customer interactions, where traditional benchmarks often fail. By emphasizing typical operational contexts instead of adversarial scenarios, INQUISITOR equips developers and engineers with a practical tool to ensure AI behaves consistently and safely within everyday tasks. The significance of INQUISITOR lies in its ability to provide a structured, repeatable system for evaluating AI behavior that fosters a shared vocabulary for communicating about failures. Its user-friendly format includes quick evaluations for instant assessments, a practitioner’s guide for routine testing, and a formal methodology for rigorous audits. By enabling stakeholders from product teams to governance functions to rely on demonstrable evidence of AI reliability, INQUISITOR facilitates smoother integration of AI technologies into practical applications and enhances the overall trustworthiness of AI systems in various settings. Moreover, with its GitHub edition available for free usage (with certain redistribution limits), it encourages widespread adoption and further innovation in the AI/ML community.