Evaluate LLM Performance Systematically
Comprehensive LLM evaluation: automated metrics (BLEU/ROUGE/BERTScore), LLM-as-judge, human annotation, A/B testing, regression detection.
Why it matters
Systematically measure and improve the performance of your Large Language Model (LLM) applications. This skill provides comprehensive evaluation strategies, from automated metrics to human assessment, ensuring your LLMs meet quality and performance standards.
Outcomes
What it gets done
Implement automated metrics (BLEU, ROUGE, BERTScore, Accuracy, etc.) for scalable evaluation.
Conduct human evaluation and LLM-as-Judge assessments for nuanced quality checks.
Detect performance regressions and validate prompt improvements.
Build confidence in production LLM systems through rigorous testing.
Install
Add it to your toolbox
Run in your project directory:
curl -fsSL https://spark.entire.vc/get/ag-llm-evaluation | bash Overview
LLM Evaluation
Comprehensive LLM evaluation covering automated metrics (BLEU, ROUGE, BERTScore, retrieval metrics), LLM-as-judge pointwise/pairwise patterns, human annotation with inter-rater agreement, statistically rigorous A/B testing, regression detection against a baseline, and benchmark harnesses. Use when systematically measuring LLM performance, comparing models/prompts, detecting regressions before deployment, or validating improvements.
What it does
Provides comprehensive evaluation strategies for LLM applications, spanning automated metrics, human evaluation, LLM-as-judge, statistical A/B testing, regression detection, and benchmarking - the full toolkit for measuring whether a model, prompt, or system change is actually better.
When to use - and when NOT to
Use this skill when systematically measuring LLM application performance, comparing models or prompts, detecting performance regressions before deployment, validating improvements from prompt changes, building confidence in production systems, establishing baselines and tracking progress, or debugging unexpected model behavior.
Inputs and outputs
Three core evaluation types: Automated metrics - text generation (BLEU for n-gram overlap, ROUGE for recall-oriented summarization scoring, METEOR, BERTScore for embedding-based similarity, perplexity), classification (accuracy, precision/recall/F1, confusion matrix, AUC-ROC), and retrieval/RAG (MRR, NDCG, Precision@K, Recall@K). Human evaluation across accuracy, coherence, relevance, fluency, safety, and helpfulness dimensions. LLM-as-judge using a stronger model to evaluate weaker outputs, either pointwise (score one response) or pairwise (compare two), reference-based or reference-free.
A quick-start example wires up an EvaluationSuite combining accuracy, BLEU, BERTScore, and a custom groundedness function against a list of test cases with expected outputs and context.
Automated metric implementations are given in full: sentence_bleu with smoothing for BLEU, rouge_scorer for ROUGE-1/2/L F-measure, bert_score for embedding-based precision/recall/F1, and custom metrics for groundedness (NLI-based entailment check against context), toxicity (via Detoxify), and factuality (claim verification against a knowledge base).
LLM-as-judge patterns show a single-output quality rater (accuracy/helpfulness/clarity on a 1-10 scale via a structured JSON prompt) and a pairwise comparator that picks a winner with reasoning and confidence:
prompt = f"""Compare these two responses...
Answer with JSON:
{{"winner": "A" or "B" or "tie", "reasoning": "...", "confidence": <1-10>}}"""
Human evaluation uses a structured AnnotationTask form (1-5 scales for accuracy/relevance/coherence, boolean issue flags for factual error/hallucination/off-topic/unsafe content) and inter-rater agreement via Cohen's kappa, interpreted from "Poor" through "Almost Perfect."
A/B testing runs a full statistical framework: independent-samples t-test for significance, Cohen's d for effect size (negligible/small/medium/large), relative improvement calculation, and a declared winner. Regression testing compares new results against a baseline per metric, flagging any relative decrease past a configurable threshold. Benchmarking runs a model against a labeled dataset across multiple metrics and aggregates mean/std/min/max per metric.
Eight best practices: use multiple metrics rather than one, test on representative real-world data, always compare against a baseline, apply proper statistical rigor, integrate evaluation into CI/CD continuously, combine automated metrics with human judgment, investigate failures via error analysis, and version-control evaluation results over time. Common pitfalls: obsessing over a single metric, drawing conclusions from too-small samples, data contamination (testing on training data), ignoring statistical variance, and metric-goal mismatch.
Integrations
Built on nltk (BLEU), rouge_score, bert_score, transformers/NLI models (groundedness), detoxify (toxicity), scikit-learn (Cohen's kappa), and scipy.stats (t-tests), with companion references (references/metrics.md, human-evaluation.md, benchmarking.md, a-b-testing.md, regression-testing.md) and assets (assets/evaluation-framework.py, assets/benchmark-dataset.jsonl, scripts/evaluate-model.py).
Who it's for
ML/LLM engineers who need to rigorously measure model or prompt performance - comparing variants with statistical significance, catching regressions before deployment, or combining automated metrics with human and LLM-as-judge evaluation - rather than eyeballing a handful of example outputs.
FAQ
Common questions
Discussion
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