[This article was first published on The Jumping Rivers Blog, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.Working with LLMs in PracticeLarge Language Models are becoming part of everyday data science work. But using them through chat interfaces is only one part of the picture.In this upcoming webinar, we focus on how to work with LLMs programmatically, using R and Python to integrate them into real workflows and applications.Secure your place by registering through the webinar registration formWhat We’ll CoverWe begin with a short introduction to how LLMs work, including how they are priced, where they perform well, and where they can fall short.From there, the session moves into practical examples of working with LLMs in code:Sending prompts to an LLM API from R using the {ellmer} packageIncluding additional instructions through system promptsStructuring prompts to return clean, tabular outputsSummarising images and PDFs using LLMsWhile the examples will focus primarily on R, we will also briefly explore the {chatlas} package for Python, which offers similar functionality.Why This MattersUsing LLMs through chat tools is useful for exploration, but it has limits.For data scientists and developers, the value comes from:Automating repetitive tasksEmbedding LLMs into applications and pipelinesGenerating structured outputs that can be reused downstreamThis webinar focuses on that shift, from interactive use to integration in code.Who Should AttendThis webinar is suitable for:Data scientists working with R or PythonDevelopers interested in integrating AI into applicationsTeams exploring how to move from experimentation to productionNo prior experience with LLM APIs is required, but familiarity with R or Python will be helpful.Webinar DetailsDate: 23rd April 2026Time: 1:15 PM (UK time)Location: OnlineCost: FreeSpeakerThe session will be led by Myles Mitchell, Principal Data Scientist at Jumping Rivers.Related Jumping Rivers TrainingIf you would like to explore these topics further, our 6-hour course, LLM-Driven Applications with R and Python covers:Building LLM-powered dashboardsSetting up retrieval-augmented generation (RAG) pipelinesResponsible use of AIJoin UsLLMs are quickly becoming part of the standard toolkit for data science.Understanding how to use them programmatically opens up far more possibilities than using them through chat alone.This session is designed to give you a clear starting point.Join us for our AI in Production conference! For more details, check out ourconference website!For updates and revisions to this article, see the original postTo leave a comment for the author, please follow the link and comment on their blog: The Jumping Rivers Blog.R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.Continue reading: Programming with LLMs in R & Python