LLMs have long outgrown their role as mere chatbots. They are opening up new possibilities in automated data processing. This talk explores how GPT models, Gemini, and Google Multilingual Embedding can be integrated into Databricks workflows for intelligent transformation, enrichment, translation, and generation, especially in multilingual use cases. It addresses common challenges such as reproducibility, deterministic outputs, pipeline robustness, and seamless integration into existing ETL processes. Best practices will also be shared to ensure that LLMs are deployed in a stable, efficient, and maintainable way within production-grade data pipelines.