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name: llm-evaluation description: "Implement comprehensive evaluation strategies for LLM applications — automated metrics (BLEU, ROUGE, BERTScore, RAG metrics), A/B testing with statistical rigor, regression detection, and benchmarking. Use when measuring agent quality, comparing models or prompts, or building eval pipelines for LangGraph or Google ADK agents." allowed-tools: Bash, Read, Write, Edit metadata: triggers: evaluate LLM, measure quality, BLEU, ROUGE, BERTScore, MRR, NDCG, A/B test prompts, regression test, benchmark model, RAG evaluation, agent quality metrics, compare prompts, eval pipeline related-skills: agentic-ai-dev, google-adk, adk-eval-guide, python-testing-patterns, vector-database domain: backend role: specialist scope: implementation output-format: code last-reviewed: "2026-03-15"

Iron Law

NO EVALUATION CONCLUSION WITHOUT A BASELINE — comparing model A to model B means nothing without a defined baseline and statistical test. Every eval claim requires: metric name, sample size, statistical significance (p < 0.05), and effect size.

LLM Evaluation Skill — Python 3.14 + LangGraph + Google ADK

When to Use This Skill

• Measuring quality of a RAG agent's retrieval or generation output
• Comparing two prompt variants or model versions before promoting to production
• Detecting metric regressions in CI after code or model changes
• Building an automated eval harness for a LangGraph or Google ADK agent
• Selecting the right metric family for a given eval task (retrieval vs generation vs classification)
• Establishing baseline scores before any optimization work
• Setting up inter-rater agreement to validate human evaluation labels

Metric Selection Quick Reference

Eval Task	Primary Metric	Secondary Metric	When NOT to Use
RAG Retrieval	MRR, NDCG@K	Precision@K, Recall@K	When you have no relevance judgments
Text Generation (summarization)	ROUGE-L, BERTScore-F1	BLEU	When output style matters more than content
Classification	F1 (macro)	Precision/Recall	When class imbalance makes accuracy misleading
A/B Prompt Comparison	Cohen's d effect size	p-value alone	When sample size < 30
RAG Generation Quality	BERTScore + Groundedness	Perplexity	For factual domain QA

Framework Decision Tree

What are you evaluating?
├── Retrieval quality (RAG) → reference/evaluation-metrics.md#rag-metrics
├── Text generation quality → reference/evaluation-metrics.md#text-generation
├── Comparing two prompts/models → reference/ab-testing.md
├── Detecting regression vs baseline → reference/ab-testing.md#regression
├── Building full eval harness → reference/evaluation-harness.md
└── Evaluating agent trajectory (tool calls, reasoning steps) → reference/trajectory-evaluation.md

Reference Files

File	Content	When to Use
reference/evaluation-metrics.md	BLEU, ROUGE, BERTScore, RAG metrics (MRR, NDCG, Precision@K), classification metrics, custom groundedness	Implementing metrics for any eval task
reference/ab-testing.md	A/B test with t-test + Cohen's d, regression detector class, sample size calculator, CI/CD integration	Comparing prompts/models, detecting regressions
reference/evaluation-harness.md	Full eval harness for LangGraph agents and ADK agents, benchmark runner, inter-rater agreement	Building end-to-end eval pipeline
reference/trajectory-evaluation.md	4-pillar evaluation (Effectiveness 40%, Efficiency 20%, Robustness 20%, Safety 20%), safety zero-tolerance, EvaluationResult structure, batch eval, pass/fail gates	Evaluating LangGraph agent trajectories end-to-end

Post-Code Review

After implementing any eval pipeline, dispatch the agentic-ai-reviewer agent to verify:

• Statistical assumptions are met (sample size, normality, independence)
• Baseline is committed and versioned (not ephemeral)
• Regression thresholds are documented and justified
• No silent metric failures (every metric error must surface, not default to 0.0)