chore: import upstream snapshot with attribution

docs/getstarted/experiments_quickstart.md

@@ -0,0 +1,100 @@
+# Run your first experiment
+
+This tutorial walks you through running your first experiment with Ragas using the `@experiment` decorator and a local CSV backend.
+
+## Prerequisites
+
+- Python 3.9+
+- Ragas installed (see [Installation](./install.md))
+
+## Hello World 👋
+
+![](/_static/imgs/experiments_quickstart/hello_world.gif)
+
+### 1. Install (if you haven’t already)
+
+```bash
+pip install ragas
+```
+
+### 2. Create `hello_world.py`
+
+Copy this into a new file and save as `hello_world.py`:
+
+```python
+import numpy as np
+from ragas import Dataset, experiment
+from ragas.metrics import MetricResult, discrete_metric
+
+
+# Define a custom metric for accuracy
+@discrete_metric(name="accuracy_score", allowed_values=["pass", "fail"])
+def accuracy_score(response: str, expected: str):
+    result = "pass" if expected.lower().strip() == response.lower().strip() else "fail"
+    return MetricResult(value=result, reason=f"Match: {result == 'pass'}")
+
+
+# Mock application endpoint that simulates an AI application response
+def mock_app_endpoint(**kwargs) -> str:
+    return np.random.choice(["Paris", "4", "Blue Whale", "Einstein", "Python"])
+
+
+# Create an experiment that uses the mock application endpoint and the accuracy metric
+@experiment()
+async def run_experiment(row):
+    response = mock_app_endpoint(query=row.get("query"))
+    accuracy = accuracy_score.score(response=response, expected=row.get("expected_output"))
+    return {**row, "response": response, "accuracy": accuracy.value}
+
+
+if __name__ == "__main__":
+    import asyncio
+
+    # Create dataset inline
+    dataset = Dataset(name="test_dataset", backend="local/csv", root_dir=".")
+    test_data = [
+        {"query": "What is the capital of France?", "expected_output": "Paris"},
+        {"query": "What is 2 + 2?", "expected_output": "4"},
+        {"query": "What is the largest animal?", "expected_output": "Blue Whale"},
+        {"query": "Who developed the theory of relativity?", "expected_output": "Einstein"},
+        {"query": "What programming language is named after a snake?", "expected_output": "Python"},
+    ]
+
+    for sample in test_data:
+        dataset.append(sample)
+    dataset.save()
+
+    # Run experiment
+    _ = asyncio.run(run_experiment.arun(dataset, name="first_experiment"))
+```
+
+### 3. Inspect the generated files
+
+```bash
+tree .
+```
+
+You should see:
+
+```
+├── datasets
+│   └── test_dataset.csv
+└── experiments
+    └── first_experiment.csv
+```
+
+### 4. View the results of your first experiment
+
+```bash
+open experiments/first_experiment.csv
+```
+
+Output preview:
+
+![](/_static/imgs/experiments_quickstart/output_first_experiment.png)
+
+## Next steps
+
+- Learn the concepts behind experiments in [Experiments (Concepts)](../concepts/experimentation.md)
+- Explore evaluation metrics in [Metrics](../concepts/metrics/index.md)
+