Evaluating LLM Applications: Fundamentals of AI Evals and Context

Building LLM applications is easy, but ensuring they generate high-quality, reliable outputs is one of the hardest challenges in AI engineering today. To bridge this gap, modern developers rely on systematic evaluations (evals) and rich context integration. This course guides you through the foundational principles of AI evaluation, showing you how to establish baseline metrics, feed relevant workspace context to your models, and interpret evaluation scores as directional feedback to guide your development. What you'll learn: - Understand the fundamental terminology of AI evaluation and LLM benchmarks - Configure context-rich inputs to improve model accuracy and relevance - Design custom evaluation criteria to test your application's specific goals - Interpret evaluation metrics as directional indicators rather than absolute scores - Apply iterative testing workflows to refine system prompts and retrieval patterns - Analyze common failure modes in LLM outputs to systematically debug your system The course begins with key terminology and the core theory of AI evals before moving into practical text-based walkthroughs that show you how to structure context and analyze performance metrics. This course is designed for beginner AI engineers, software developers, and product builders looking to make their AI systems more reliable, with no prior evaluation experience required. Start reading today to build a rigorous, metrics-driven approach to your AI engineering workflow.