AI Observability and Evaluation Fundamentals with Braintrust

Building AI applications is easy, but ensuring they perform reliably in production is a major challenge. This written course introduces you to the core principles of AI observability, showing you how to track, evaluate, and refine your LLM outputs systematically. You will transition from guessing how your AI performs to measuring its success with concrete data. By learning how to use Braintrust, you will gain the skills to run rigorous evaluations, version your prompts, compare model performance, and catch regressions before they impact your users. In this course, you will: 1. Understand the core concepts of AI observability, tracing, and evaluation metrics. 2. Configure Braintrust to log, track, and analyze LLM inputs and outputs in real time. 3. Design automated evaluation datasets to measure accuracy, latency, and cost. 4. Iterate on prompt engineering systematically using version control and comparison tools. 5. Compare different foundation models to choose the best fit for your specific use case. 6. Apply modern observability patterns to Retrieval-Augmented Generation (RAG) pipelines. The course starts with fundamental definitions of AI evaluation before guiding you through written step-by-step implementation guides, prompt playground workflows, and production monitoring strategies. This course is designed for software developers, aspiring AI engineers, and product builders who are new to AI observability; no prior experience with LLM evaluation tools is required. Start reading today to bring engineering rigor to your AI applications.