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.
Master comprehensive evaluation strategies for LLM applications, from automated metrics to human evaluation and A/B testing.
Fast, repeatable, scalable evaluation using computed scores.
Text Generation:
Classification:
Retrieval (RAG):
Manual assessment for quality aspects difficult to automate.
Dimensions:
Use stronger LLMs to evaluate weaker model outputs.
Approaches:
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)
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
)
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
}
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()
}
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
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))
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))
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))
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": ""
}
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
}
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"
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"
}
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']}")
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()
}