annotate-traces-for-review

What problem does it solve?

This Skill simplifies the process of setting up human review and annotation of LLM traces, allowing domain experts to label outputs, perform error analysis, and build a comprehensive dataset for better AI model evaluation.

Core Features & Use Cases

• Human-in-the-Loop Review: Triggered by specific prompts to facilitate human review of LLM outputs.
• Error Analysis: Cluster failure reasons into categories for targeted improvement.
• Dataset Building: Use annotated data to create golden datasets for model evaluation.
• Use Case: When automated evaluations are insufficient, use this Skill to analyze LLM outputs in high-stakes or specialized domains.

Quick Start

Use the 'annotate-traces-for-review' skill to begin reviewing LLM outputs for a specific model.