The author developed an AI document pipeline to prevent hallucinations by moving away from free-text generation. It uses strict JSON schemas, domain-specific validation rules, and human-in-the-loop approval to catch errors before documents reach customers.
This paper proposes an auditable climate risk intelligence framework for validating fragmented ESG data, integrating deterministic orchestration, temporal anomaly detection, and imbalance-aware ensemble learning. It introduces a synthetic ESG validation benchmark to support open reproducibility.
This article introduces a method to automate ETL data validation efficiently, significantly reducing the reliance on extensive SQL scripting. It promises to streamline the data quality process, allowing validation to be completed in minutes.