Experiments: Running Experiments in Python
Execute experiments with run_experiment.
Basic Usage
from phoenix.client import Client
from phoenix.client.experiments import run_experiment
client = Client()
dataset = client.datasets.get_dataset(name="qa-test-v1")
def my_task(example):
return call_llm(example.input["question"])
def exact_match(output, expected):
return 1.0 if output.strip().lower() == expected["answer"].strip().lower() else 0.0
experiment = run_experiment(
dataset=dataset,
task=my_task,
evaluators=[exact_match],
experiment_name="qa-experiment-v1",
)Task Functions
# Basic task
def task(example):
return call_llm(example.input["question"])
# With context (RAG)
def rag_task(example):
return call_llm(f"Context: {example.input['context']}\nQ: {example.input['question']}")Evaluator Parameters
| Parameter | Access |
|---|---|
output |
Task output |
expected |
Example expected output |
input |
Example input |
metadata |
Example metadata |
Options
experiment = run_experiment(
dataset=dataset,
task=my_task,
evaluators=evaluators,
experiment_name="my-experiment",
dry_run=3, # Test with 3 examples
repetitions=3, # Run each example 3 times
)Results
print(experiment.aggregate_scores)
# {'accuracy': 0.85, 'faithfulness': 0.92}
for run in experiment.runs:
print(run.output, run.scores)Stability
Single-run scores are noisy when either the task or the evaluator is non-deterministic — an LLM call, tool use, streaming output, an LLM-as-judge. On a small dataset, that per-run noise can swamp the signal from a prompt change.
Averaging over repetitions lets the score you report reflect the prompt rather than the sampling noise:
run_experiment(
# ...
repetitions=3,
)Things to consider:
- Reach for repetitions when the task or the evaluator is an LLM call and the dataset is small.
- Prefer repetitions when per-example cost is low and you mostly want to settle the score; prefer growing the dataset when you also need to cover more behaviors.
- Skip repetitions when both the task and the evaluator are deterministic (e.g. string comparison against a ground truth) — a single run is the answer.
Consider adding stability when:
- Repeat runs of the same experiment drift in ways that feel larger than the differences you're trying to measure.
- A prompt change flips example labels in ways that don't track with how the outputs actually changed.
- The judge's reasoning on the same output reads differently from one run to the next.
Repetitions are also what repetitions=1 (default) silently relies on — don't trust a tuning decision based on a single 10-example run.
Add Evaluations Later
from phoenix.client.experiments import evaluate_experiment
evaluate_experiment(experiment=experiment, evaluators=[new_evaluator])