LLM Evaluation Fundamentals

Overview

Core metrics and methodologies for evaluating language model quality. Rigorous evaluation is essential for building trustworthy AI applications.

Why Evaluation Matters

Without proper evaluation, you cannot:

Know if your model is actually good

Catch regressions before users do

Make data-driven decisions about model improvements

Meet regulatory and compliance requirements

Evaluation Framework

python
from dataclasses import dataclass, field
from typing import Callable, Optional
import statistics
@dataclass
class EvalExample:
    """Single evaluation example."""
    id: str
    input: str
    expected_output: str
    metadata: dict = field(default_factory=dict)
@dataclass
class EvalResult:
    """Result for a single evaluation."""
    example_id: str
    model_output: str
    score: float
    metrics: dict = field(default_factory=dict)
    passed: bool = True
    notes: str = ""
class Evaluator:
    """LLM Evaluation Fundamentals evaluator."""
    
    def __init__(self, model_fn: Callable, metrics: list[Callable]):
        self.model_fn = model_fn
        self.metrics = metrics
    
    def evaluate_single(self, example: EvalExample) -> EvalResult:
        """Evaluate one example."""
        output = self.model_fn(example.input)
        
        scores = {}
        for metric in self.metrics:
            score = metric(output, example.expected_output)
            scores[metric.__name__] = score
        
        overall_score = statistics.mean(scores.values()) if scores else 0.0
        
        return EvalResult(
            example_id=example.id,
            model_output=output,
            score=overall_score,
            metrics=scores,
            passed=overall_score >= 0.7
        )
    
    def evaluate_dataset(self, examples: list[EvalExample]) -> dict:
        """Evaluate a full dataset."""
        results = [self.evaluate_single(ex) for ex in examples]
        
        scores = [r.score for r in results]
        passed = [r for r in results if r.passed]
        failed = [r for r in results if not r.passed]
        
        return {
            "total": len(results),
            "passed": len(passed),
            "failed": len(failed),
            "pass_rate": len(passed) / len(results),
            "avg_score": statistics.mean(scores),
            "min_score": min(scores),
            "max_score": max(scores),
            "p50": statistics.median(scores),
            "results": results
        }

Key Metrics Implementation

python
from openai import OpenAI
import re
client = OpenAI()
def rouge_l_score(prediction: str, reference: str) -> float:
    """Compute ROUGE-L similarity."""
    pred_tokens = set(prediction.lower().split())
    ref_tokens = set(reference.lower().split())
    
    if not ref_tokens:
        return 0.0
    
    intersection = pred_tokens & ref_tokens
    precision = len(intersection) / max(len(pred_tokens), 1)
    recall = len(intersection) / len(ref_tokens)
    
    if precision + recall == 0:
        return 0.0
    return 2 * precision * recall / (precision + recall)
def llm_judge_score(prediction: str, reference: str, criteria: str = "quality") -> float:
    """Use GPT-4 as an automated judge."""
    prompt = f"""Rate the following AI response on a scale of 1-10.
Criteria: {criteria}
Reference answer: {reference}
AI response: {prediction}
Rating (just the number 1-10):"""
    
    resp = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": prompt}],
        temperature=0,
        max_tokens=5
    )
    
    try:
        score = float(re.findall(r'\d+', resp.choices[0].message.content)[0])
        return min(max(score / 10, 0), 1.0)
    except:
        return 0.5
def exact_match(prediction: str, reference: str) -> float:
    """Exact string match score."""
    return 1.0 if prediction.strip().lower() == reference.strip().lower() else 0.0

Running Evaluations

python
Create test dataset
test_examples = [
    EvalExample(
        id="test_001",
        input="What is the capital of France?",
        expected_output="Paris",
        metadata={"category": "geography", "difficulty": "easy"}
    ),
    EvalExample(
        id="test_002", 
        input="Explain how neural networks learn",
        expected_output="Neural networks learn through backpropagation...",
        metadata={"category": "ml_concepts", "difficulty": "medium"}
    ),
]
Define model function
def my_model(input_text: str) -> str:
    resp = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": input_text}],
        temperature=0,
        max_tokens=500
    )
    return resp.choices[0].message.content
Create evaluator with metrics
evaluator = Evaluator(
    model_fn=my_model,
    metrics=[rouge_l_score, exact_match]
)
Run evaluation
results = evaluator.evaluate_dataset(test_examples)
print(f"Pass rate: {results['pass_rate']:.1%}")
print(f"Average score: {results['avg_score']:.3f}")
print(f"Failed: {results['failed']} / {results['total']}")

Continuous Evaluation in CI/CD

yaml
.github/workflows/eval.yml
name: LLM Evaluation
on: [push, pull_request]
jobs:
  evaluate:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Run evaluations
        run: python -m eval.run_suite
        env:
          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
          PASS_THRESHOLD: "0.8"
      - name: Post results
        uses: actions/github-script@v7
        with:
          script: |
            const results = require('./eval_results.json')
            github.rest.issues.createComment({...})

Best Practices

Separate test sets — never evaluate on training data

Track over time — catch regressions early

Human spot-check — automated metrics miss subtleties

Diverse examples — cover edge cases and failure modes

Version test suites — track test suite changes separately

Resources

RAGAS documentation: https://docs.ragas.io

Eleuther AI lm-evaluation-harness: https://github.com/EleutherAI/lm-evaluation-harness

HELM benchmark: https://crfm.stanford.edu/helm/