AI Engineering: Evaluating LLM Performance in Braintrust

Building reliable AI applications requires more than just trial-and-error prompting; you must systematically measure and evaluate your model's outputs. This text-based course guides you through the foundational concepts of AI evaluation and teaches you how to track performance metrics effectively. You will transition from guessing if your prompts work to analyzing model runs, comparing inputs and outputs, and leveraging structured evaluation scores to optimize your AI systems. What you'll learn: - Understand the fundamental terminology of AI evaluation and LLM performance tracking. - Configure and run evaluation scripts to generate structured performance scores. - Analyze inputs, outputs, and system prompts within the Braintrust dashboard. - Compare different model runs to identify regressions and performance improvements. - Apply modern evaluation metrics to assess accuracy, latency, and cost. - Manage test datasets to ensure consistent and reproducible AI benchmarking. This course begins with core evaluation concepts and foundational definitions before guiding you through running evaluation scripts and interpreting dashboard analytics. It is designed for aspiring AI engineers and developers new to LLM evaluation, requiring no prior experience with Braintrust. Start mastering AI evaluation and build more reliable LLM applications today.