Large language models are quickly becoming part of everyday work and changing how organizations interpret data, collaborate, and even build software. **Data sensemaking** describes the process of turning complex, messy information into something understandable and actionable in everyday work. With the increased use of large language models (LLMs), numerous teams are experimenting with novel approaches to handling data, code, and text. In this talk [Viktoriia Olshanskaia](mention:58e4f672-8354-4c7e-a258-ed27cef9540d "/u/vik.olsh/") , a Junior Researcher at LUT University in Finland, shares insights into her research. **Together, we will explore the following questions:** - What is data sensemaking? - How do civic tech groups integrate LLMs into data sensemaking workflows? - What factors enable or constrain the adoption and use of LLMs? - How has code churn in civic tech projects changed over the last decade, particularly in the era of generative AI? This talk is for anyone who wants to know what data sensemaking is, how LLMs can be used in existing workflows to be beneficial, and where they won't be. It is also for anyone who is ready for an English talk in the summer! :ice cream::sun::speech balloon: In June Viktoriia will be a guest at **Weizenbaum Institut Berlin** and is also looking for organisations who would like to take part in an interview for her reasearch. If you're interested [check out this post](https://community.civic-data.de/content/perma?id=29421).