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LangSmith in 10 Minutes
LangChain. A guided tour of an LLM trace, project, and dataset by LangChain's co-founder — token cost, latency, error rate, feedback aggregation, drilling into a single retrieval-step span. It's the closest visual analogue to what the article describes when it talks about "every call is a span" and why structured traces beat print logging.
AI Expert note
It is a vendor walkthrough — watch it for the trace, project and dataset concepts rather than the product pitch; the 'every call is a span' idea transfers to any tracing stack.
What you should get from this
You can navigate traces, projects and datasets in LangSmith and read off token cost, latency, error rate and per-span detail.
Watch or know first
Basic familiarity with LLM API calls; you don't need to be a LangChain user to follow the trace concepts.
Last reviewed: May 18, 2026
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