Understanding LLM Observability
Why You Need LLM Observability
LLM applications are probabilistic and non-deterministic. This makes them unreliable. This video explains why observability is critical for building reliable AI applications.
What you'll learn:
- Debug AI agents by viewing complete traces with inputs and outputs for each step
- Build a data flywheel to improve your application iteratively using production data
- Manage costs by understanding which steps and users consume the most resources
OpenTelemetry for LLM Observability
This video covers the technical implementation of LLM observability using OpenTelemetry.
What you'll learn:
- How traces work and why they matter for LLM applications
- OpenTelemetry standard that lets you swap observability platforms without code changes
- Auto-instrumentation libraries that add observability with one line of code
- Higher-level SDKs from platforms like Agenta that simplify implementation