tracing
4 free lessons tagged tracing across Programming, AI. Each one is a short sequence of focused steps with narration and a five-question quiz at the end — take them in any order, no signup required.
Distributed Tracing and the Art of Throwing Data Away
Tracing every request through every service produces the most useful telemetry you have and more of it than anyone can afford. This lesson covers how context propagation actually stitches a trace together, the head versus tail sampling decision and why it determines which incidents you can debug, and the collector pipeline where all of it is enforced.
Metrics, Logs, Traces: Three Signals, Three Cost Models
Observability is not a product you buy but a property your system has: can you explain a behaviour you did not predict? This lesson defines the three telemetry signals, what question each answers, why their costs grow along completely different axes, and why the difference between monitoring and observability is the difference between known and unknown failure modes.
LLM Observability with OpenTelemetry: GenAI Semantic Conventions
Master the OTel GenAI semantic conventions — gen_ai.* attributes, span structure for prompts/completions/tools, sampling strategies, and cost attribution — and understand why standardizing across LangSmith, Phoenix, Datadog, and Grafana matters for production AI systems.
LangSmith: Tracing & Evaluating Your LLM Applications
Dive into LangSmith, the developer platform for building and evaluating robust Large Language Model (LLM) applications. Learn how to trace execution paths, debug complex chains, and rigorously evaluate your LLM's performance to ensure reliability and quality.