Quality Assurance
SKILL.md · 5 documents
LLM Evaluation
LLM output evaluation — automated metrics, LLM-as-judge, A/B testing, regression testing. Use when measuring LLM output quality, comparing prompt or model versions, building an automated eval pipeline, setting up regression tests for prompt changes, or evaluating RAG systems and bias/safety.
LLM Evaluation
LLM output evaluation — automated metrics, LLM-as-judge, A/B testing, regression testing. Use when measuring LLM output quality, comparing prompt or model versions, building an automated eval pipeline, setting up regression tests for prompt changes, or evaluating RAG systems and bias/safety.
Prompt Testing Skill
description: Comprehensive prompt testing and LLM output evaluation skill covering hallucination detection, response quality scoring, regression testing for prompts, A/B testing, and building evaluation pipelines for AI-powered applications.
Evaluation Framework Reference
[Task Definitions] → [Model Interface] → [Inference Execution] → [Scoring] → [Reports]
LLM Evaluation
description: Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.