Projects
Use projects to organize your LLM traces
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Use projects to organize your LLM traces
Last updated
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Projects provide organizational structure for your AI applications, allowing you to logically separate your observability data. This separation is essential for maintaining clarity and focus.
With Projects, you can:
Segregate traces by environment (development, staging, production)
Isolate different applications or use cases
Track separate experiments without cross-contamination
Maintain dedicated evaluation spaces for specific initiatives
Create team-specific workspaces for collaborative analysis
Projects act as containers that keep related traces and conversations together while preventing them from interfering with unrelated work. This organization becomes increasingly valuable as you scale - allowing you to easily switch between contexts without losing your place or mixing data.
The Project structure also enables comparative analysis across different implementations, models, or time periods. You can run parallel versions of your application in separate projects, then analyze the differences to identify improvements or regressions.