> 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/architecture.md). # Architecture * **Data Collection**: Gather raw data from various sources, including text, images, audio, etc. * **Data Cleaning**: Remove duplicates, irrelevant or erroneous data, and handle missing or anomalous values. * **Data Annotation**: Annotate the data as needed to ensure accuracy and consistency. ## Initial Data Filtering * **Data Preprocessing**: Perform preprocessing steps such as normalization, standardization, or tokenization. * **Data Classification**: Classify the data based on features and labels to perform initial filtering and identify potentially useful datasets. ## Data Evaluation Layer * **Define Evaluation Metrics**: Establish metrics for assessing data quality, such as accuracy, completeness, consistency, and relevance. * **Develop Evaluation Models**: Create or select appropriate evaluation models, which may include rule-based systems, statistical methods, or machine learning models. * **Rule-Based Evaluation**: Set rules and standards to assess data quality. * **Statistical Methods**: Use statistical methods to detect anomalies and biases in the data. * **Machine Learning Models**: Train machine learning models to evaluate data quality. * **Execute Evaluation**: Apply the evaluation models to the data, generating quality reports and scores. * **Feedback Loop**: Adjust data collection and processing workflows based on evaluation results to improve data quality. ## Data Selection and Filtering * **Filter Based on Evaluation Results**: Select high-quality datasets based on evaluation outcomes. * **Data Integration**: Integrate the filtered datasets into the training dataset, ensuring consistency and high quality.