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agents/plugins/llm-application-dev/skills/llm-evaluation/SKILL.md
Seth Hobson 47a5dbc3f9 fix(skills): remove phantom resource references and fix CoC links (#447) 
Remove references to non-existent resource files (references/, assets/,
scripts/, examples/) from 115 skill SKILL.md files. These sections
pointed to directories and files that were never created, causing
confusion when users install skills.

Also fix broken Code of Conduct links in issue templates to use
absolute GitHub URLs instead of relative paths that 404.
2026-03-07 10:53:17 -05:00

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---
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
```python
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)
```
## Automated Metrics Implementation
### BLEU Score
```python
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
```python
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
```python
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
```python
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
```python
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-4-6",
max_tokens=500,
system=system,
messages=[{"role": "user", "content": prompt}]
)
return QualityRating(**json.loads(message.content[0].text))
```
### Pairwise Comparison
```python
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-4-6",
max_tokens=500,
messages=[{"role": "user", "content": prompt}]
)
return ComparisonResult(**json.loads(message.content[0].text))
```
### Reference-Based Evaluation
```python
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-4-6",
max_tokens=500,
messages=[{"role": "user", "content": prompt}]
)
return ReferenceEvaluation(**json.loads(message.content[0].text))
```
## Human Evaluation Frameworks
### Annotation Guidelines
```python
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
```python
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
```python
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
```python
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
```python
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-4-6", "version": "1.0.0"}
)
print(f"Mean score: {experiment_results.aggregate_metrics['qa']['mean']}")
```
## Benchmarking
### Running Benchmarks
```python
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()
}
```
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