annotate-traces-for-review

Set up human review for LLM outputs by using a framework that facilitates human-in-the-loop annotation triggered by specific prompts. This allows domain experts to label outputs, manage context, and perform structured reviews for specialized domains.

Perform error analysis on LLM traces by clustering failure reasons into distinct categories. This targeted improvement approach helps domain experts identify specific model weaknesses during the human review workflow.

You need human-in-the-loop annotation when automated evaluations are insufficient for your high-stakes or specialized domain. It provides accurate labeling and context management where automated metrics fail to capture nuanced domain expertise.

Yes, you can cluster LLM failure reasons into categories for targeted improvement during the human review process. This helps systematically organize error analysis and structure the dataset building process.

Build a golden dataset for AI model evaluation by applying structured review workflows to label LLM traces. Domain experts use these annotated outputs to form a comprehensive dataset that enhances subsequent model evaluation.