Uploaded July 2025 | Updated September 2026, 3 weeks ago
In this tutorial, you’ll learn how to build a custom human annotation interface for Phoenix using Lovable and use those annotations to run experiments and evaluate your application.
A custom annotation UI makes it easy to collect structured human feedback on traces directly in Phoenix, enabling faster iteration and improvement of your LLM systems. By establishing this feedback loop, you can effectively monitor and enhance your application’s performance.
Find the notebook here: arize.com/docs/phoenix/cookbook/tracing-and-annotations/using-human-annotations-for-eval-driven-development
Join our community slack: arize.com/community
Get started with Phoenix for free: app.arize.com/auth/phoenix/signup
More on Phoenix annotations: arize.com/docs/phoenix/tracing/features-tracing/how-to-annotate-traces
In this tutorial, you’ll learn how to build a custom human annotation interface for Phoenix using Lovable and use those annotations to run experiments and evaluate your application.
A custom annotation UI makes it easy to collect structured human feedback on traces directly in Phoenix, enabling faster iteration and improvement of your LLM systems. By establishing this feedback loop, you can effectively monitor and enhance your application’s performance.
Find the notebook here: arize.com/docs/phoenix/cookbook/tracing-and-annotations/using-human-annotations-for-eval-driven-development
Join our community slack: arize.com/community
Get started with Phoenix for free: app.arize.com/auth/phoenix/signup
More on Phoenix annotations: arize.com/docs/phoenix/tracing/features-tracing/how-to-annotate-traces
