Annotation by humans with expertise is the gold standard for helping to improve AI systems. In this tutorial, you’ll learn how to build a custom human annotation interface 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.
✍️ Notebook
✍️ More on how to annotate LLM traces
✍️ More about annotation for strong AI development pipelines
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