Developed and delivered AI-powered data validation features for the elementary-data/dbt-data-reliability repository, focusing on scalable data quality checks across Snowflake, Databricks, and BigQuery. Introduced a macro for validating data against user-defined expectations and a test wrapper for unstructured data, leveraging LLM integration to automate validation and reduce manual effort. Standardized parameter handling and naming conventions, removed legacy code, and updated documentation to clarify support for both structured and unstructured data. Utilized SQL, dbt, and Jinja to implement these solutions, ensuring alignment with business goals and improving adoption through clear technical writing and comprehensive documentation updates.