Prompt Evaluation Quickstart ↗

ragas quickstart prompt_evals
cd prompt_evals

uv sync

export OPENAI_API_KEY="your-openai-key"

uv run python evals.py

prompt_evals/
├── README.md              # Project documentation
├── pyproject.toml         # Project configuration
├── prompt.py              # Prompt implementation
├── evals.py               # Evaluation workflow
├── __init__.py            # Python package marker
└── evals/
    ├── datasets/          # Test datasets
    ├── experiments/       # Evaluation results
    └── logs/              # Execution logs

from prompt import run_prompt

sentiment = run_prompt("I loved the movie! It was fantastic.")
# Returns: "positive" or "negative"

@discrete_metric(name="accuracy", allowed_values=["pass", "fail"])
def my_metric(prediction: str, actual: str):
    return (
        MetricResult(value="pass", reason="")
        if prediction == actual
        else MetricResult(value="fail", reason="")
    )

dataset_dict = [
    {"text": "I loved the movie! It was fantastic.", "label": "positive"},
    {"text": "The movie was terrible and boring.", "label": "negative"},
    # More examples...
]

# Version 1: Simple
prompt = f"Is this positive or negative: {text}"

# Version 2: With examples
prompt = f"""Classify sentiment:
Examples:
- "Great movie" -> positive
- "Boring film" -> negative

Text: {text}
Sentiment:"""

# Compare results across versions

from ragas.metrics import NumericalMetric

confidence = NumericalMetric(
    name="confidence",
    prompt="Rate confidence 1-5 in this classification: {prediction}",
    allowed_values=(1, 5),
)