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Learning Objectives & Key Takeaways
0 / 4 Completed- Understand LangSmith core architecture and runtime execution model
- Audit real-world latency, throughput benchmarks, and memory footprint
- Implement zero-downtime deployment patterns with resilience guards
- Verify code snippets and architect blueprints against production SLOs
Practise it next
LangSmith interview questions, quiz and handbook in your Learning Dashboard
What you will learn
- A trace shows every prompt, tool call, token count and latency in one tree.
- Datasets turn real failures into permanent regression tests.
- Evaluators score outputs automatically before you ship a prompt change.
You cannot fix what you cannot see
LLM bugs rarely throw exceptions. The model quietly receives the wrong context, a tool returns an empty list, or a prompt edit makes answers longer and vaguer. Without traces, the team argues from screenshots.
LangSmith records each run as a tree: the chain, every model call with its exact input and output, tool calls, timings and token usage. Debugging becomes reading, not guessing.
In the rest of this story
- Tracing in two lines
- Tracing your own code
- From traces to evals
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