Llm Evaluation

---
name: llm-evaluation
description: Master comprehensive evaluation strategies for LLM applications, from automated metrics to human evaluation and A/B testing. 
category: AI & Agents
source: antigravity
tags: [python, api, ai, llm, gpt, rag, cro]
url: https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/llm-evaluation
---


# LLM Evaluation

Master comprehensive evaluation strategies for LLM applications, from automated metrics to human evaluation and A/B testing.

## Do not use this skill when

- The task is unrelated to llm evaluation
- You need a different domain or tool outside this scope

## Instructions

- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
- If detailed examples are required, open `resources/implementation-playbook.md`.

## Use this skill when

- 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 llm_eval import EvaluationSuite, Metric

# Define evaluation suite
suite = EvaluationSuite([
    Metric.accuracy(),
    Metric.bleu(),
    Metric.bertscore(),
    Metric.custom(name="groundedness", fn=check_groundedness)
])

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

# Run evaluation
results = suite.evaluate(
    model=your_model,
    test_cases=test_cases
)

print(f"Overall Accuracy: {results.metrics['accuracy']}")
print(f"BLEU Score: {results.metrics['bleu']}")
```

## Automated Metrics Implementation

### BLEU Score
```python
from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction

def calculate_bleu(reference, hypothesis):
    """Calculate BLEU score between reference and hypothesis."""
    smoothie = SmoothingFunction().method4

    return sentence_bleu(
        [reference.split()],
        hypothesis.split(),
        smoothing_function=smoothie
    )

# Usage
bleu = calculate_bleu(
    reference="The cat sat on the mat",
    hypothesis="A cat is sitting on the mat"
)
```

### ROUGE Score
```python
from rouge_score import rouge_scorer

def calculate_rouge(reference, hypothesis):
    """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, hypotheses):
    """Calculate BERTScore using pre-trained BERT."""
    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, context):
    """Check if response is grounded in provided context."""
    # Use NLI model to check entailment
    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):
    """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