Evaluate LLM outputs accuracy relevance and hallucination
Scanned 9/10/2026
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---
name: llm-evaluation
description: "Evaluate LLM outputs accuracy relevance and hallucination"
---
# LLM Evaluation
## Automated Checks
```python
# Relevance
def check_relevance(answer, context):
return len([w for w in context.split() if w in answer]) / len(context.split())
# Hallucination: does answer contradict context?
def check_hallucination(answer, context):
# Use NLI model or LLM judge
pass
```
## Metrics
| Metric | What It Measures |
|--------|-----------------|
| BLEU | N-gram overlap |
| ROUGE | Recall of key phrases |
| BERTScore | Semantic similarity |
| LLM-as-judge | Overall quality |
## Evaluation Dataset
Split into correct/incorrect examples, score accuracy.
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