# Context Precision RAG Metric

**Objective**: This metric calculates the ratio of the total number of relevant documents retrieved out of the total number of retrieved documents. The test measures the proportion of available contextual information that could prove useful in answering the prompt.

**Required Parameters**: `Prompt`, `Expected Response`, `Context`

**Interpretation**: A higher score signifies more major proportion of contexts supplied to the LLM helped answer the prompt question

<figure><img src="/files/hhmlGDOx3IprsuOeXcrM" alt=""><figcaption></figcaption></figure>

**Code Execution:**

```python
metrics=[
    {"name": "Context Precision", "config": {"model": "gpt-4o-mini", "provider": "openai"}, "column_name": "your-column-identifier", "schema_mapping": schema_mapping}
]
```

The "schema\_mapping" variable needs to be defined first and is a pre-requisite for evaluation runs. Learn how to set this variable [here](/ragaai-catalyst/concepts/running-ragaai-evals/executing-evaluations.md).

**Example**:

* Prompt: What is the tallest mountain in the world?
* Expected Response: The tallest mountain in the world is Mount Everest, which has a peak that reaches 8,848 metres (29,029 feet) above sea level.
* Context: \[‘Mount Everest is the tallest mountain in the world, with a peak that reaches 8,848 metres (29,029 feet) above sea level.’,’The Himalayas, where Mount Everest is located, is a mountain range in Asia, separating the plains of the Indian subcontinent from the Tibetan Plateau.’,’K2, also known as Mount Godwin-Austen, is the second-highest mountain in the world and is part of the Karakoram Range.’]
* *Metric Output*: {‘score’: 0.33, ‘reason’: ‘Only one context is directly relevant to answering the prompt. The other two contexts, while related to mountains, do not directly address the question about the tallest mountain.’}


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/rag-metrics/context-precision.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.