# Experiments: Overview
Systematic testing of AI systems with datasets, tasks, and evaluators.
## Structure
```
DATASET → Examples: {input, expected_output, metadata}
TASK → function(input) → output
EVALUATORS → (input, output, expected) → score
EXPERIMENT → Run task on all examples, score results
```
## Basic Usage
```python
from phoenix.client.experiments import run_experiment
experiment = run_experiment(
dataset=my_dataset,
task=my_task,
evaluators=[accuracy, faithfulness],
experiment_name="improved-retrieval-v2",
)
print(experiment.aggregate_scores)
# {'accuracy': 0.85, 'faithfulness': 0.92}
```
## Workflow
1. **Create dataset** - From traces, synthetic data, or manual curation
2. **Define task** - The function to test (your LLM pipeline)
3. **Select evaluators** - Code and/or LLM-based
4. **Run experiment** - Execute and score
5. **Analyze & iterate** - Review, modify task, re-run
## Dry Runs
Test setup before full execution:
```python
experiment = run_experiment(dataset, task, evaluators, dry_run=3) # Just 3 examples
```
## Best Practices
- **Name meaningfully**: `"improved-retrieval-v2-2024-01-15"` not `"test"`
- **Version datasets**: Don't modify existing
- **Multiple evaluators**: Combine perspectives
