Foundations of AI Evaluation: Assessing Narrow AI and Language Models

As artificial intelligence continues to transform industries, understanding how to measure and evaluate the effectiveness of basic AI systems is a critical skill. This text-based course guides you through the foundational concepts of early-stage AI, focusing on Narrow AI and the core principles of system evaluation. By the end of this course, you will transition from a curious beginner to a practitioner who can confidently analyze AI outputs, understand key performance metrics, and identify common biases or errors in early-stage deployments. What you'll learn: Understand the core definitions of Narrow AI and how it differs from general intelligence; Analyze fundamental evaluation metrics including accuracy, precision, recall, and F1-score; Evaluate modern large language models using basic prompt testing and output verification techniques; Identify common limitations, biases, and hallucinations in foundational AI systems; Apply structured evaluation frameworks to real-world text-based scenarios. The course starts with clear definitions of first-stage AI systems before progressing to practical evaluation methodologies. You will read through real-world case studies and complete written exercises designed to build your analytical skills. This course is designed for absolute beginners, business analysts, and aspiring product managers who want to understand how AI is assessed without needing a background in coding. Start reading today to build a strong foundation in AI evaluation.