Debugging Generative AI Applications with OpenTelemetry Tracing

Generative AI applications can be highly unpredictable, making traditional debugging methods ineffective. Understanding how data flows through your prompts, retrieval systems, and LLM calls is essential for building production-ready AI systems. This course teaches you how to implement structured tracing and observability in your generative AI applications. You will learn to track complex execution flows, identify latency bottlenecks, and debug non-deterministic outputs using industry-standard open tools. What you'll learn: Understand foundational observability concepts and how tracing applies to large language models; Configure OpenTelemetry to capture detailed execution paths across your AI application components; Trace retrieval-augmented generation workflows to pinpoint search and generation failures; Analyze latency, token usage, and prompt execution steps to optimize application performance; Implement structured logging and span attributes tailored for generative AI workloads. We begin with the core terminology of tracing and observability before walking through step-by-step written implementations. You will practice configuring spans, tracking nested LLM calls, and evaluating trace data through practical written exercises and code snippets. This course is designed for beginner software developers and AI enthusiasts looking to make their generative AI applications reliable, with no prior experience in tracing required. Start reading today to bring transparency and reliability to your generative AI workflows.