llm-evaluation
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.
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SKILL.md
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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