Fundamentals

Application-specific tests for AI systems. Code first, LLM for nuance, human for truth.

Evaluator Types

Type	Speed	Cost	Use Case
Code	Fast	Cheap	Regex, JSON, format, exact match
LLM	Medium	Medium	Subjective quality, complex criteria
Human	Slow	Expensive	Ground truth, calibration

Decision: Code first → LLM only when code can't capture criteria → Human for calibration.

Score Structure

Property	Required	Description
`name`	Yes	Evaluator name
`kind`	Yes	`"code"`, `"llm"`, `"human"`
`score`	No*	0-1 numeric
`label`	No*	`"pass"`, `"fail"`
`explanation`	No	Rationale

*One of score or label required.

Binary > Likert

Use pass/fail, not 1-5 scales. Clearer criteria, easier calibration.

# Multiple binary checks instead of one Likert scale
evaluators = [
    AnswersQuestion(),    # Yes/No
    UsesContext(),        # Yes/No
    NoHallucination(),    # Yes/No
]

Quick Patterns

Code Evaluator

from phoenix.evals import create_evaluator

@create_evaluator(name="has_citation", kind="code")
def has_citation(output: str) -> bool:
    return bool(re.search(r'\[\d+\]', output))

LLM Evaluator

from phoenix.evals import ClassificationEvaluator, LLM

evaluator = ClassificationEvaluator(
    name="helpfulness",
    prompt_template="...",
    llm=LLM(provider="openai", model="gpt-4o"),
    choices={"not_helpful": 0, "helpful": 1}
)

Run Experiment

from phoenix.client.experiments import run_experiment

experiment = run_experiment(
    dataset=dataset,
    task=my_task,
    evaluators=[evaluator1, evaluator2],
)
print(experiment.aggregate_scores)

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