llm-evaluation

name	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.

LLM Evaluation

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

When to Use This Skill

• 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 dataclasses import dataclass
from typing import Callable
import numpy as np

@dataclass
class Metric:
    name: str
    fn: Callable

    @staticmethod
    def accuracy():
        return Metric("accuracy", calculate_accuracy)

    @staticmethod
    def bleu():
        return Metric("bleu", calculate_bleu)

    @staticmethod
    def bertscore():
        return Metric("bertscore", calculate_bertscore)

    @staticmethod
    def custom(name: str, fn: Callable):
        return Metric(name, fn)

class EvaluationSuite:
    def __init__(self, metrics: list[Metric]):
        self.metrics = metrics

    async def evaluate(self, model, test_cases: list[dict]) -> dict:
        results = {m.name: [] for m in self.metrics}

         test  test_cases:
            prediction =  model.predict(test[])

             metric  .metrics:
                score = metric.fn(
                    prediction=prediction,
                    reference=test.get(),
                    context=test.get()
                )
                results[metric.name].append(score)

         {
            : {k: np.mean(v)  k, v  results.items()},
            : results
        }


suite = EvaluationSuite([
    Metric.accuracy(),
    Metric.bleu(),
    Metric.bertscore(),
    Metric.custom(, check_groundedness)
])

test_cases = [
    {
        : ,
        : ,
        : 
    },
]

results =  suite.evaluate(model=your_model, test_cases=test_cases)

Automated Metrics Implementation

BLEU Score

from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction

def calculate_bleu(reference: str, hypothesis: str, **kwargs) -> float:
    """Calculate BLEU score between reference and hypothesis."""
    smoothie = SmoothingFunction().method4

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

ROUGE Score

from rouge_score import rouge_scorer

def calculate_rouge(reference: str, hypothesis: str, **kwargs) -> dict:
    """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: list[str],
    hypotheses: list[str],
    **kwargs
) -> dict:
    """Calculate BERTScore using pre-trained model."""
    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: str, context: str, **kwargs) -> float:
    """Check if response is grounded in provided context."""
    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: str, **kwargs) -> float:
    """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: str, sources: list[str], **kwargs) -> float:
    """Verify factual claims against sources."""
    from transformers import pipeline

    nli = pipeline("text-classification", model="facebook/bart-large-mnli")

    scores = []
     source  sources:
        result = nli()[]
         result[] == :
            scores.append(result[])

     (scores)  scores  

LLM-as-Judge Patterns

Single Output Evaluation

from anthropic import Anthropic
from pydantic import BaseModel, Field
import json

class QualityRating(BaseModel):
    accuracy: int = Field(ge=1, le=10, description="Factual correctness")
    helpfulness: int = Field(ge=1, le=10, description="Answers the question")
    clarity: int = Field(ge=1, le=10, description="Well-written and understandable")
    reasoning: str = Field(description="Brief explanation")

async def llm_judge_quality(
    response: str,
    question: str,
    context: str = None
) -> QualityRating:
    """Use Claude to judge response quality."""
    client = Anthropic()

    system = """You are an expert evaluator of AI responses.
    Rate responses on accuracy, helpfulness, and clarity (1-10 scale).
    Provide brief reasoning for your ratings."""

    prompt = f"""Rate the following response:

Question: {question}
{f'Context: {context}' if context else ''}
Response: {response}

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

    message = client.messages.create(
        model="claude-sonnet-4-5",
        max_tokens=,
        system=system,
        messages=[{: , : prompt}]
    )

     QualityRating(**json.loads(message.content[].text))

Pairwise Comparison

from pydantic import BaseModel, Field
from typing import Literal

class ComparisonResult(BaseModel):
    winner: Literal["A", "B", "tie"]
    reasoning: str
    confidence: int = Field(ge=1, le=10)

async def compare_responses(
    question: str,
    response_a: str,
    response_b: str
) -> ComparisonResult:
    """Compare two responses using LLM judge."""
    client = Anthropic()

    prompt = f"""Compare these two responses and determine which is better.

Question: {question}

Response A: {response_a}

Response B: {response_b}

Consider accuracy, helpfulness, and clarity.

Answer with JSON:
{{
  "winner": "A" or "B" or "tie",
  "reasoning": "<explanation>",
  "confidence": <1-10>
}}"""

    message = client.messages.create(
        model="claude-sonnet-4-5",
        max_tokens=500,
        messages=[{"role": "user", "content": prompt}]
    )

    return ComparisonResult(**json.loads(message.content[0].text))

Reference-Based Evaluation

class ReferenceEvaluation(BaseModel):
    semantic_similarity: float = Field(ge=0, le=1)
    factual_accuracy: float = Field(ge=0, le=1)
    completeness: float = Field(ge=0, le=1)
    issues: list[str]

async def evaluate_against_reference(
    response: str,
    reference: str,
    question: str
) -> ReferenceEvaluation:
    """Evaluate response against gold standard reference."""
    client = Anthropic()

    prompt = f"""Compare the response to the reference answer.

Question: {question}
Reference Answer: {reference}
Response to Evaluate: {response}

Evaluate:
1. Semantic similarity (0-1): How similar is the meaning?
2. Factual accuracy (0-1): Are all facts correct?
3. Completeness (0-1): Does it cover all key points?
4. List any specific issues or errors.

Respond in JSON:
{{
  "semantic_similarity": <0-1>,
  "factual_accuracy": <0-1>,
  "completeness": <0-1>,
  "issues": ["issue1", "issue2"]
}}"""

    message = client.messages.create(
        model="claude-sonnet-4-5",
        max_tokens=500,
        messages=[{"role": "user", "content": prompt}]
    )

    return ReferenceEvaluation(**json.loads(message.content[0].text))

Human Evaluation Frameworks

Annotation Guidelines

from dataclasses import dataclass, field
from typing import Optional

@dataclass
class AnnotationTask:
    """Structure for human annotation task."""
    response: str
    question: str
    context: Optional[str] = None

    def get_annotation_form(self) -> dict:
        return {
            "question": self.question,
            "context": self.context,
            "response": self.response,
            "ratings": {
                "accuracy": {
                    "scale": "1-5",
                    "description": "Is the response factually correct?"
                },
                "relevance": {
                    "scale": "1-5",
                    "description": "Does it answer the question?"
                },
                "coherence": {
                    "scale": "1-5",
                    "description": "Is it logically consistent?"
                }
            },
            "issues": {
                "factual_error": False,
                "hallucination": ,
                : ,
                : 
            },
            : 
        }

Inter-Rater Agreement

from sklearn.metrics import cohen_kappa_score

def calculate_agreement(
    rater1_scores: list[int],
    rater2_scores: list[int]
) -> dict:
    """Calculate inter-rater agreement."""
    kappa = cohen_kappa_score(rater1_scores, rater2_scores)

    if kappa < 0:
        interpretation = "Poor"
    elif kappa < 0.2:
        interpretation = "Slight"
    elif kappa < 0.4:
        interpretation = "Fair"
    elif kappa < 0.6:
        interpretation = "Moderate"
    elif kappa < 0.8:
        interpretation = "Substantial"
    else:
        interpretation = "Almost Perfect"

    return {
        "kappa": kappa,
        "interpretation": interpretation
    }

A/B Testing

Statistical Testing Framework

from scipy import stats
import numpy as np
from dataclasses import dataclass, field

@dataclass
class ABTest:
    variant_a_name: str = "A"
    variant_b_name: str = "B"
    variant_a_scores: list[float] = field(default_factory=list)
    variant_b_scores: list[float] = field(default_factory=list)

    def add_result(self, variant: str, score: float):
        """Add evaluation result for a variant."""
        if variant == "A":
            self.variant_a_scores.append(score)
        else:
            self.variant_b_scores.append(score)

    def analyze(self, alpha: float = 0.05) -> dict:
        """Perform statistical analysis."""
        a_scores = np.array(self.variant_a_scores)
        b_scores = np.array(self.variant_b_scores)

        # T-test
        t_stat, p_value = stats.ttest_ind(a_scores, b_scores)

        # Effect size (Cohen's d)
        pooled_std = np.sqrt((np.std(a_scores)**2 + np.std(b_scores)**2) / 2)
        cohens_d = (np.mean(b_scores) - np.mean(a_scores)) / pooled_std

         {
            : np.mean(a_scores),
            : np.mean(b_scores),
            : np.mean(b_scores) - np.mean(a_scores),
            : (np.mean(b_scores) - np.mean(a_scores)) / np.mean(a_scores),
            : p_value,
            : p_value < alpha,
            : cohens_d,
            : ._interpret_cohens_d(cohens_d),
            : .variant_b_name  np.mean(b_scores) > np.mean(a_scores)  .variant_a_name
        }


     () -> :
        
        abs_d = (d)
         abs_d < :
             
         abs_d < :
             
         abs_d < :
             
        :
             

Regression Testing

Regression Detection

from dataclasses import dataclass

@dataclass
class RegressionResult:
    metric: str
    baseline: float
    current: float
    change: float
    is_regression: bool

class RegressionDetector:
    def __init__(self, baseline_results: dict, threshold: float = 0.05):
        self.baseline = baseline_results
        self.threshold = threshold

    def check_for_regression(self, new_results: dict) -> dict:
        """Detect if new results show regression."""
        regressions = []

        for metric in self.baseline.keys():
            baseline_score = self.baseline[metric]
            new_score = new_results.get(metric)

            if new_score is None:
                continue

            # Calculate relative change
            relative_change = (new_score - baseline_score) / baseline_score

            # Flag if significant decrease
            is_regression = relative_change < -self.threshold
            if is_regression:
                regressions.append(RegressionResult(
                    metric=metric,
                    baseline=baseline_score,
                    current=new_score,
                    change=relative_change,
                    is_regression=True
                ))

         {
            : (regressions) > ,
            : regressions,
            : 
        }

LangSmith Evaluation Integration

from langsmith import Client
from langsmith.evaluation import evaluate, LangChainStringEvaluator

# Initialize LangSmith client
client = Client()

# Create dataset
dataset = client.create_dataset("qa_test_cases")
client.create_examples(
    inputs=[{"question": q} for q in questions],
    outputs=[{"answer": a} for a in expected_answers],
    dataset_id=dataset.id
)

# Define evaluators
evaluators = [
    LangChainStringEvaluator("qa"),           # QA correctness
    LangChainStringEvaluator("context_qa"),   # Context-grounded QA
    LangChainStringEvaluator("cot_qa"),       # Chain-of-thought QA
]

# Run evaluation
async def target_function(inputs: dict) -> dict:
    result = await your_chain.ainvoke(inputs)
    return {"answer": result}

experiment_results = await evaluate(
    target_function,
    data=dataset.name,
    evaluators=evaluators,
    experiment_prefix="v1.0.0",
    metadata={"model": "claude-sonnet-4-5", "version": "1.0.0"}
)

print(f"Mean score: {experiment_results.aggregate_metrics['qa']['mean']}")

Benchmarking

Running Benchmarks

from dataclasses import dataclass
import numpy as np

@dataclass
class BenchmarkResult:
    metric: str
    mean: float
    std: float
    min: float
    max: float

class BenchmarkRunner:
    def __init__(self, benchmark_dataset: list[dict]):
        self.dataset = benchmark_dataset

    async def run_benchmark(
        self,
        model,
        metrics: list[Metric]
    ) -> dict[str, BenchmarkResult]:
        """Run model on benchmark and calculate metrics."""
        results = {metric.name: [] for metric in metrics}

        for example in self.dataset:
            # Generate prediction
            prediction = await model.predict(example["input"])

            # Calculate each metric
            for metric in metrics:
                score = metric.fn(
                    prediction=prediction,
                    reference=example["reference"],
                    context=example.get("context")
                )
                results[metric.name].append(score)

        # Aggregate results
         {
            metric: BenchmarkResult(
                metric=metric,
                mean=np.mean(scores),
                std=np.std(scores),
                =(scores),
                =(scores)
            )
             metric, scores  results.items()
        }

Resources

• LangSmith Evaluation Guide
• RAGAS Framework
• DeepEval Library
• Arize Phoenix
• HELM Benchmark

Best Practices

1. Multiple Metrics: Use diverse metrics for comprehensive view
2. Representative Data: Test on real-world, diverse examples
3. Baselines: Always compare against baseline performance
4. Statistical Rigor: Use proper statistical tests for comparisons
5. Continuous Evaluation: Integrate into CI/CD pipeline
6. Human Validation: Combine automated metrics with human judgment
7. Error Analysis: Investigate failures to understand weaknesses
8. Version Control: Track evaluation results over time

Common Pitfalls

• Single Metric Obsession: Optimizing for one metric at the expense of others
• Small Sample Size: Drawing conclusions from too few examples
• Data Contamination: Testing on training data
• Ignoring Variance: Not accounting for statistical uncertainty
• Metric Mismatch: Using metrics not aligned with business goals
• Position Bias: In pairwise evals, randomize order
• Overfitting Prompts: Optimizing for test set instead of real use