> For the complete documentation index, see [llms.txt](https://flareai.gitbook.io/flareai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://flareai.gitbook.io/flareai/core-mechanics/overview.md).

# Overview

FlareAI is a technical solution that enables data providers to supply training samples while allowing AI users to access higher-quality training data. Below are the key evaluation dimensions and methods we apply to data:

**Evaluation Dimensions:**

* **Accuracy**: Ensures data correctness and proper labeling.
* **Completeness**: Checks for full data coverage with no missing values.
* **Consistency**: Verifies uniformity across different sources and times.
* **Relevance**: Assesses data suitability for specific tasks.
* **Stability**: Ensures stable data distribution over time.
* **Diversity**: Evaluates coverage of all relevant scenarios.

**Evaluation Methods:**

* **Statistical Analysis**: Uses statistical tools to assess data quality.
* **Data Visualization**: Employs visual methods to detect patterns and anomalies.
* **Data Quality Scoring**: Combines dimensions into an overall quality score.
* **Automated Tools**: Utilizes technology for automated data quality checks.

This concise approach ensures that FlareAI consistently delivers high-quality training data.