Create, execute, and monitor LLM and code evaluators on Arize.
npx skills add https://github.com/ethan-rio/skills --skill arize-evaluator
Please help me install this Agent Skill. Skill: arize-evaluator Source: https://github.com/ethan-rio/skills/tree/main/skills/arize/arize-evaluator Command: npx skills add https://github.com/ethan-rio/skills --skill arize-evaluator
Manually setting up LLM evaluators, scoring spans or experiment runs, and configuring continuous monitoring on Arize is time-consuming and prone to configuration errors like incorrect column mappings or missing credentials. This Skill eliminates that manual overhead by guiding you through end-to-end evaluation workflows with built-in validation and error handling.
Use the arize-evaluator skill to create a hallucination checker for your Arize project and run a backfill evaluation on the last 100 spans to validate scoring accuracy.
High-intent search queries and answers about installing and using this skill.
Automating LLM evaluation on Arize is done by creating, versioning, and executing LLM-as-judge and code evaluators to continuously score incoming project spans and backfill historical traces.
An LLM-as-judge evaluator uses an LLM template to score tasks like hallucination detection and retrieval relevance. You need it for RAG validation to automatically assess answer correctness for user queries.
To backfill evaluation scores on historical Arize spans, you run one-time backfill evaluations using the Arize ax CLI, applying custom column mappings to match your specific data schema and trace history.
Yes, you can run both deterministic code and LLM template evaluators on Arize experiment dataset runs, enabling you to score correctness, validate formats, and track performance across project spans.
Yes, the Arize ax CLI supports custom column mapping for continuous span scoring, allowing you to align the evaluation logic with your specific data schema and prevent configuration errors.
Arize LLM evaluator configurations often fail during span scoring due to incorrect column mappings or missing credentials, which can be prevented by using guided end-to-end workflows with built-in validation.
Arize-aiExport and analyze Arize traces and spans by ID for debugging LLM applications.
GAIR-NLPRun a preflight-gated evolution loop with evaluator scoring and experiment databases.
qcmuuCapture OpenTelemetry traces and evaluation results for LLM debugging.
ethan-rioManage versioned ML evaluation datasets in Arize via the ax CLI.
