llm-evals

What problem does it solve?

LLMs produce non-deterministic outputs, so prompt tweaks or model updates can silently break existing functionality without any visible warning signs. Manual review of LLM responses is slow, subjective, and impossible to scale across frequent changes, leaving teams with no reliable way to catch quality regressions before they impact users.

Core Features & Use Cases

• Comprehensive Eval Coverage: Implements patterns for unit, model, agent, and system-level evaluations tailored to different LLM workflow speeds and determinism requirements.
• Tool Integration: Includes ready-to-use code samples and configurations for leading eval tools including LangSmith, RAGAS, PromptFoo, DeepEval, and Braintrust.
• Production Guardrails: Provides golden dataset management best practices, LLM-as-judge bias mitigation, CI integration templates, and regression threshold enforcement to prevent bad deployments.
• Real-World Use Case: A team building a RAG customer support chatbot can use this Skill to track faithfulness and context recall metrics, run adversarial tests for prompt injection, and automatically fail CI builds if quality drops below defined thresholds.

Quick Start

Use the llm-evals skill to configure automated evaluation checks that catch LLM output regressions in your CI pipeline before they reach production.