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Description

Large language models (LLMs) enable powerful data-driven applications, but many projects get stuck in “proof-of-concept purgatory”—where flashy demos fail to translate into reliable, production-ready software. This talk introduces the LLM software development lifecycle (SDLC)—a structured approach to moving beyond early-stage prototypes. Using first principles from software engineering, observability, and iterative evaluation, we’ll cover common pitfalls, techniques for structured output extraction, and methods for improving reliability in real-world data applications. Attendees will leave with concrete strategies for integrating AI into software—ensuring LLMs generate value beyond the prototype stage.

Instructor's Bio

Hugo Bowne-Anderson, PhD

Independent Data and AI Consultant 

Hugo Bowne-Anderson is an independent data and AI consultant with extensive experience in the tech industry. He is the host of [Vanishing Gradients](https://vanishinggradients.fireside.fm/) and [High Signal](https://high-signal.delphina.ai/), podcasts exploring developments in data science and AI. Previously, Hugo served as Head of Developer Relations at Outerbounds and held roles at Coiled and DataCamp, where his work in data science education reached over 3 million learners. He has taught at Yale University, Cold Spring Harbor Laboratory, and conferences like SciPy and PyCon, and is a passionate advocate for democratizing data skills and open-source tools. He also regularly teaches courses on [Building LLM Applications for Data Scientists and Software Engineers](https://maven.com/s/course/d56067f338).

Webinar

  • 1

    Talk "Escaping Proof-of-Concept Purgatory: Building Robust LLM-Powered Applications"

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