# Precision\@K
**Objective:**
Precision\@K measures how many of the top K retrieved or generated items are relevant to a query. It is a rank-based evaluation metric commonly used in information retrieval and recommendation systems. A higher Precision\@K score means the model has higher relevance among its top K results, indicating strong early retrieval performance, particularly when precision in high-rank positions is crucial.
**Required Columns in Dataset:**
`Prompt`, `Ranked Context`, `Labeled Text`
**Interpretation:**
* **High Precision\@K**: Shows that the top K results are highly relevant to the query, indicating the model's effectiveness in prioritizing the most appropriate outputs early in the ranking.
* **Low Precision\@K**: Suggests that the top K results contain irrelevant information, reflecting poor retrieval performance in terms of precision.
**Execution via UI:**
**Execution via SDK:**
Precision\_K doesn't require LLM for computation.
```python
metrics=[
{"name": "Precision_K", "column_name": "your-text", "schema_mapping": schema_mapping}
]
```
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