Chalk recently won Best Technology at The GenAI Collective's Demo Night, where Elliot showcased a tool that merges structured data with large language model (LLM) analysis in a unified feature store. This was demonstrated by ingesting financial transactions from a traditional database and employing Gemini to analyze unstructured memo lines for retrieving merchant categories, cleaning memos, and extracting additional insights. The implementation of this feature pipeline involves using Python and the Chalk framework, which simplifies prompt engineering by handling structured data and caching responses to reduce API costs. The demo, which can be found on GitHub, highlighted Chalk's capability to facilitate the creation of feature pipelines, and the event was celebrated alongside other members of the ML/AI community in San Francisco.