Safeguarding Enterprise Integrity: The Vital Role of Continuous Data Integrity Checks for LLM Apps

Intuitive visualization of performance metrics versus meta-features, facilitating in-depth analysis and data authenticity.

Export clusters and plots for further analysis or integration with external tools.

Probability distribution plots to highlight outliers in numerical and categorical meta-features, ensuring data quality.

Clear insights into data distribution and deviations from norms for detailed examination or integration into downstream processes.

Unknown token detection to aid in identifying model hallucinations by highlighting tokens not present in the training corpus.

Generate re-training datasets with discovered tokens, facilitating model refinement and improved performance.

Probability distributions of meta-features like toxicity, language, and sentiment provide valuable insights into data properties.

Export functionalities to extract outlier information or global insights for further investigation or reporting purposes.