LLM Evaluation

Seth HobsonCommunityCreated by Seth Hobson

Skill
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Data and AI
MIT
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From the author

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.

— the description by Seth Hobson

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured approach to assessing the quality and reliability of LLM-based applications. It covers three primary evaluation pillars: automated metrics for scalable performance tracking, human evaluation for nuanced quality assessment, and LLM-as-a-judge patterns for automated qualitative scoring. The skill includes implementation patterns for common metrics like BLEU, ROUGE, and BERTScore, as well as custom logic for groundedness and factuality checking.

What it does

Implements automated metrics for text generation, classification, and RAG retrieval tasks.
Provides a Python-based evaluation suite structure for running test cases against models.
Offers patterns for using stronger LLMs to act as judges for response quality.
Includes custom metric implementations for groundedness, toxicity, and factuality.
Defines dimensions for human-in-the-loop evaluation such as coherence and safety.
Supports comparative analysis and regression testing for prompt and model iterations.

When to use it

When you need to establish a baseline for LLM application performance.
When comparing different models or prompt variations systematically.
When you need to detect performance regressions before deploying updates.
When validating the factual accuracy or groundedness of RAG-based responses.
When building confidence in production systems through repeatable testing.

Advantages

Enables scalable, repeatable evaluation of AI outputs.
Reduces manual effort by automating common quality checks.
Provides a modular architecture for adding custom evaluation metrics.
Supports both quantitative scoring and qualitative reasoning via LLM-as-a-judge.
Helps identify specific error patterns through confusion matrices and detailed scoring.

Usage examples

“How can I implement a custom metric to check if my RAG response is grounded in the provided context?”: The skill provides a Python function using a cross-encoder model to calculate entailment scores, which can be integrated into the EvaluationSuite.
“How do I use an LLM to grade the quality of my chatbot's responses?”: The skill demonstrates an LLM-as-a-judge pattern using a structured Pydantic model to extract numerical ratings and reasoning from a stronger model.

Install

Download the skill as a ZIP:
https://autogineer.com/agent-skills/wshobson/llm-evaluation/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip llm-evaluation.zip -d .agents/skills

Documentation by Seth Hobson

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}

        for test in test_cases:
            prediction = await model.predict(test["input"])

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

        return {
            "metrics": {k: np.mean(v) for k, v in results.items()},
            "raw_scores": results
        }

# Usage
suite = EvaluationSuite([
    Metric.accuracy(),
    Metric.bleu(),
    Metric.bertscore(),
    Metric.custom("groundedness", check_groundedness)
])

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

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

Detailed patterns and worked examples

Detailed pattern documentation lives in references/details.md. Read that file when the navigation tier above is insufficient.

Files

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references/details.md

llm-evaluation — detailed patterns and worked examples

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 = []
    for source in sources:
        result = nli(f"{source}</s></s>{claim}")[0]
        if result['label'] == 'entailment':
            scores.append(result['score'])

    return max(scores) if scores else 0.0

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-5",
        max_tokens=500,
        system=system,
        messages=[{"role": "user", "content": prompt}]
    )

    return QualityRating(**json.loads(message.content[0].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-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-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": False,
                "off_topic": False,
                "unsafe_content": False
            },
            "feedback": ""
        }

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

        return {
            "variant_a_mean": np.mean(a_scores),
            "variant_b_mean": np.mean(b_scores),
            "difference": np.mean(b_scores) - np.mean(a_scores),
            "relative_improvement": (np.mean(b_scores) - np.mean(a_scores)) / np.mean(a_scores),
            "p_value": p_value,
            "statistically_significant": p_value < alpha,
            "cohens_d": cohens_d,
            "effect_size": self._interpret_cohens_d(cohens_d),
            "winner": self.variant_b_name if np.mean(b_scores) > np.mean(a_scores) else self.variant_a_name
        }

    @staticmethod
    def _interpret_cohens_d(d: float) -> str:
        """Interpret Cohen's d effect size."""
        abs_d = abs(d)
        if abs_d < 0.2:
            return "negligible"
        elif abs_d < 0.5:
            return "small"
        elif abs_d < 0.8:
            return "medium"
        else:
            return "large"

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
                ))

        return {
            "has_regression": len(regressions) > 0,
            "regressions": regressions,
            "summary": f"{len(regressions)} metric(s) regressed"
        }

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-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
        return {
            metric: BenchmarkResult(
                metric=metric,
                mean=np.mean(scores),
                std=np.std(scores),
                min=min(scores),
                max=max(scores)
            )
            for metric, scores in results.items()
        }

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Python

Security

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Attribution and licence

Created by

Seth Hobson

Publisher

Seth Hobson (Community)

Source

wshobson/agents · plugins/llm-application-dev/skills/llm-evaluation · commit 4bde316

Licence

MIT License (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-09

AutoGineer is not affiliated with or endorsed by Seth Hobson or Seth Hobson.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download llm-evaluation.zip

From the author

AutoGineer summary

What it does

When to use it

Advantages

Usage examples

Install

Documentation by Seth Hobson

LLM Evaluation

When to Use This Skill

Core Evaluation Types

1. Automated Metrics

2. Human Evaluation

3. LLM-as-Judge

Quick Start

Detailed patterns and worked examples

Files

references/details.md

llm-evaluation — detailed patterns and worked examples

Automated Metrics Implementation

BLEU Score

ROUGE Score

BERTScore

Custom Metrics

LLM-as-Judge Patterns

Single Output Evaluation

Pairwise Comparison

Reference-Based Evaluation

Human Evaluation Frameworks

Annotation Guidelines

Inter-Rater Agreement

A/B Testing

Statistical Testing Framework

Regression Testing

Regression Detection

LangSmith Evaluation Integration

Benchmarking

Running Benchmarks

Requirements and compatibility

Security

Attribution and licence

Related skills

Claude API

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Vision Sft

Lora Qlora Recipes

Preference Optimization

Download ZIP