Evaluate & Test AI Using Real-World Work

This practical field manual provides a comprehensive guide for evaluating and testing self-improving systems using real-world scenarios. Key areas covered include: • Methods for exposing hidden system failures • Techniques for assessing operational risks • Strategies for building reliable systems • Compliance with regulatory requirements The book outlines the LLM INQUISITOR Methodology, focusing on giving language models real work under realistic conditions to observe their behavior. This approach is crucial for understanding system limitations and ensuring stability before deployment. Learn how to conduct thorough evaluations that go beyond superficial testing, identifying vulnerabilities and biases that could impact performance. The guidance is aligned with emerging regulatory frameworks, helping users build systems that are not only robust but also compliant. This manual is essential for developers, engineers, and project managers responsible for deploying and maintaining sophisticated language models and other adaptable systems, especially those operating in regulated industries.