# Evaluators: Pre-Built
Use for exploration only. Validate before production.
## Python
```python
from phoenix.evals import HallucinationEvaluator, QAEvaluator, RelevanceEvaluator, LLM
llm = LLM(provider="openai", model="gpt-4o")
hallucination_eval = HallucinationEvaluator(llm)
```
## TypeScript
```typescript
import { createHallucinationEvaluator } from "@arizeai/phoenix-evals";
import { openai } from "@ai-sdk/openai";
const hallucinationEval = createHallucinationEvaluator({ model: openai("gpt-4o") });
```
## Available
Hallucination, QA, Relevance, Toxicity, Faithfulness, Summarization.
## When to Use
| Situation | Recommendation |
| --------- | -------------- |
| Exploration | Find traces to review |
| Find outliers | Sort by scores |
| Production | Validate first (>80% human agreement) |
| Domain-specific | Build custom |
## Exploration Pattern
```python
results = run_evals(traces, [hallucination_eval])
low_scores = results[results["score"] < 0.5] # Review these
high_scores = results[results["score"] > 0.9] # Also sample
```
## Validation Required
```python
from sklearn.metrics import classification_report
print(classification_report(human_labels, evaluator_results["label"]))
# Target: >80% agreement
```
