LLM-powered data classification for data entities at scale

This post details the evolution of Grab's data classification system, Gemini, to incorporate LLM-powered column-level tagging. It describes the challenges with previous approaches (manual classification, third-party tools with false positives) and how LLMs address these by providing a natural language interface for data governance personnel. The methodology includes an orchestration service that manages requests, aggregates them into batches, and applies rate limiting. The classification process focuses on LLM-powered column-level tag classification, defining the task as assigning tags to schema fields based on an internal classification scheme. A detailed prompt example for the LLM is provided, along with a curated tag library. Prompt engineering techniques like articulating requirements and few-shot learning are highlighted as crucial for achieving a fixed output format.