LLM Evaluation

Master comprehensive evaluation strategies for LLM applications, from automated metrics to human evaluation and A/B testing.

Do not use this skill when

• The task is unrelated to llm evaluation
• You need a different domain or tool outside this scope

Instructions

• Clarify goals, constraints, and required inputs.
• Apply relevant best practices and validate outcomes.
• Provide actionable steps and verification.
• If detailed examples are required, open resources/implementation-playbook.md.

Use this skill when

• Measuring LLM application performance systematically
• Comparing different models or prompts
• Detecting performance regressions before deployment
• Validating improvements from prompt changes
• Building confidence in production systems
• Establishing baselines and tracking progress over time
• Debugging unexpected model behavior

Core Evaluation Types

1. Automated Metrics

Fast, repeatable, scalable evaluation using computed scores.

Text Generation:

• BLEU: N-gram overlap (translation)
• ROUGE: Recall-oriented (summarization)
• METEOR: Semantic similarity
• BERTScore: Embedding-based similarity
• Perplexity: Language model confidence

Classification:

• Accuracy: Percentage correct
• Precision/Recall/F1: Class-specific performance
• Confusion Matrix: Error patterns
• AUC-ROC: Ranking quality

Retrieval (RAG):

• MRR: Mean Reciprocal Rank
• NDCG: Normalized Discounted Cumulative Gain
• Precision@K: Relevant in top K
• Recall@K: Coverage in top K

2. Human Evaluation

Manual assessment for quality aspects difficult to automate.

Dimensions:

• Accuracy: Factual correctness
• Coherence: Logical flow
• Relevance: Answers the question
• Fluency: Natural language quality
• Safety: No harmful content
• Helpfulness: Useful to the user

3. LLM-as-Judge

Use stronger LLMs to evaluate weaker model outputs.

Approaches:

• Pointwise: Score individual responses
• Pairwise: Compare two responses
• Reference-based: Compare to gold standard
• Reference-free: Judge without ground truth

Quick Start

from llm_eval import EvaluationSuite, Metric

# Define evaluation suite
suite = EvaluationSuite([
    Metric.accuracy(),
    Metric.bleu(),
    Metric.bertscore(),
    Metric.custom(name="groundedness", fn=check_groundedness)
])

# Prepare test cases
test_cases = [
    {
        "input": "What is the capital of France?",
        "expected": "Paris",
        "context": "France is a country in Europe. Paris is its capital."
    },
    # ... more test cases
]

# Run evaluation
results = suite.evaluate(
    model=your_model,
    test_cases=test_cases
)

print(f"Overall Accuracy: {results.metrics['accuracy']}")
print(f"BLEU Score: {results.metrics['bleu']}")

Automated Metrics Implementation

BLEU Score

from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction

def calculate_bleu(reference, hypothesis):
    """Calculate BLEU score between reference and hypothesis."""
    smoothie = SmoothingFunction().method4

    return sentence_bleu(
        [reference.split()],
        hypothesis.split(),
        smoothing_function=smoothie
    )

# Usage
bleu = calculate_bleu(
    reference="The cat sat on the mat",
    hypothesis="A cat is sitting on the mat"
)

ROUGE Score

from rouge_score import rouge_scorer

def calculate_rouge(reference, hypothesis):
    """Calculate ROUGE scores."""
    scorer = rouge_scorer.RougeScorer(['rouge1', 'rouge2', 'rougeL'], use_stemmer=True)
    scores = scorer.score(reference, hypothesis)

    return {
        'rouge1': scores['rouge1'].fmeasure,
        'rouge2': scores['rouge2'].fmeasure,
        'rougeL': scores['rougeL'].fmeasure
    }

BERTScore

from bert_score import score

def calculate_bertscore(references, hypotheses):
    """Calculate BERTScore using pre-trained BERT."""
    P, R, F1 = score(
        hypotheses,
        references,
        lang='en',
        model_type='microsoft/deberta-xlarge-mnli'
    )

    return {
        'precision': P.mean().item(),
        'recall': R.mean().item(),
        'f1': F1.mean().item()
    }

Custom Metrics

def calculate_groundedness(response, context):
    """Check if response is grounded in provided context."""
    # Use NLI model to check entailment
    from transformers import pipeline

    nli = pipeline("text-classification", model="microsoft/deberta-large-mnli")

    result = nli(f"{context} [SEP] {response}")[0]

    # Return confidence that response is entailed by context
    return result['score'] if result['label'] == 'ENTAILMENT' else 0.0

def calculate_toxicity(text):
    """Measure toxicity in generated text."""
    from detoxify import Detoxify

    results = Detoxify('original').predict(text)
    return max(results.values())  # Return highest toxicity score

def calculate_factuality(claim, knowledge_base):
    """Verify factual claims against knowledge base."""
    # Implementation depends on your knowledge base
    # Could use retrieval + NLI, or fact-checking API
    pass

LLM-as-Judge Patterns

Single Output Evaluation

def llm_judge_quality(response, question):
    """Use GPT-5 to judge response quality."""
    prompt = f"""Rate the following response on a scale of 1-10 for:
1. Accuracy (factually correct)
2. Helpfulness (answers the question)
3. Clarity (well-written and understandable)

Question: {question}
Response: {response}

Provide ratings in JSON format:
{{
  "accuracy": <1-10>,
  "helpfulness": <1-10>,
  "clarity": <1-10>,
  "reasoning": "<brief explanation>"
}}
"""

    result