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plugins/phoenix/skills/phoenix-evals/references/evaluators-llm-python.md

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+# Evaluators: LLM Evaluators in Python
+
+LLM evaluators use a language model to judge outputs. Use when criteria are subjective.
+
+## Quick Start
+
+```python
+from phoenix.evals import ClassificationEvaluator, LLM
+
+llm = LLM(provider="openai", model="gpt-4o")
+
+HELPFULNESS_TEMPLATE = """Rate how helpful the response is.
+
+<question>{{input}}</question>
+<response>{{output}}</response>
+
+"helpful" means directly addresses the question.
+"not_helpful" means does not address the question.
+
+Your answer (helpful/not_helpful):"""
+
+helpfulness = ClassificationEvaluator(
+    name="helpfulness",
+    prompt_template=HELPFULNESS_TEMPLATE,
+    llm=llm,
+    choices={"not_helpful": 0, "helpful": 1}
+)
+```
+
+## Template Variables
+
+Use XML tags to wrap variables for clarity:
+
+| Variable | XML Tag |
+| -------- | ------- |
+| `{{input}}` | `<question>{{input}}</question>` |
+| `{{output}}` | `<response>{{output}}</response>` |
+| `{{reference}}` | `<reference>{{reference}}</reference>` |
+| `{{context}}` | `<context>{{context}}</context>` |
+
+## create_classifier (Factory)
+
+Shorthand factory that returns a `ClassificationEvaluator`. Prefer direct
+`ClassificationEvaluator` instantiation for more parameters/customization:
+
+```python
+from phoenix.evals import create_classifier, LLM
+
+relevance = create_classifier(
+    name="relevance",
+    prompt_template="""Is this response relevant to the question?
+<question>{{input}}</question>
+<response>{{output}}</response>
+Answer (relevant/irrelevant):""",
+    llm=LLM(provider="openai", model="gpt-4o"),
+    choices={"relevant": 1.0, "irrelevant": 0.0},
+)
+```
+
+## Input Mapping
+
+Column names must match template variables. Rename columns or use `bind_evaluator`:
+
+```python
+# Option 1: Rename columns to match template variables
+df = df.rename(columns={"user_query": "input", "ai_response": "output"})
+
+# Option 2: Use bind_evaluator
+from phoenix.evals import bind_evaluator
+
+bound = bind_evaluator(
+    evaluator=helpfulness,
+    input_mapping={"input": "user_query", "output": "ai_response"},
+)
+```
+
+## Running
+
+```python
+from phoenix.evals import evaluate_dataframe
+
+results_df = evaluate_dataframe(dataframe=df, evaluators=[helpfulness])
+```
+
+## Best Practices
+
+1. **Be specific** - Define exactly what pass/fail means
+2. **Include examples** - Show concrete cases for each label
+3. **Explanations by default** - `ClassificationEvaluator` includes explanations automatically
+4. **Study built-in prompts** - See
+   `phoenix.evals.__generated__.classification_evaluator_configs` for examples
+   of well-structured evaluation prompts (Faithfulness, Correctness, DocumentRelevance, etc.)