# Subjective Question Correction
**Objective:**
The SQC Score evaluates generative models by measuring their ability to transform incorrectly or ambiguously phrased questions into valid ones. It uses both syntactic and semantic criteria to assess how well the model corrects and reframes user inputs. A high score reflects strong contextual understanding and linguistic alignment between the generated question and expected output, important for enhancing user interaction in query-driven systems.
**Required Columns in Dataset:**
`Prompt`, `Expected Response (GT)` , `LLM Response`
**Interpretation:**
* **High SQC Score**: Reflects that the model successfully corrects ambiguous or flawed questions, offering outputs that are aligned with the desired user intent.
* **Low SQC Score**: Indicates poor performance in understanding or reformulating incorrect questions, potentially leading to irrelevant or incoherent corrections.
**Execution via UI:**
**Execution via SDK:**
```python
metrics=[
{"name": "SQCScore", "config": {"model": "gpt-4o-mini", "provider": "openai"}, "column_name": "your-text", "schema_mapping": schema_mapping}
]
```
---
# 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/information-extraction/subjective-question-correction.md?ask=
```
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.