2025年12月6日 –, Main Stream 語言: English
This talk demystifies Large Language Models (LLMs) by presenting them not as magic, but as powerful APIs that developers can master using existing software engineering skills. It advocates for an engineering-based approach, demonstrating how to use Python's abstract base classes to create a vendor-agnostic and testable interface. This allows for interchangeable backends, from cloud services like Gemini to local mocks, making AI development more modular and robust. The talk empowers developers to look past the hype, and instead apply their existing knowledge to build reliable and innovative applications, concluding with the message that with LLMs, "It's just an API. It's just Python. Grab a key, write a test, and build the future."
his talk by Anna Eilering, a Developer Relations Engineer at Google's Open Source Programs Office, cuts through the hype surrounding Large Language Models (LLMs) and Generative AI. The core message is that LLMs are not magic, but powerful APIs that developers can and should work with just like any other code. The talk is aimed at Python developers who may feel intimidated by the mystique around AI, and encourages them to leverage their existing skills to build innovative solutions.
I'm a Senior Developer Relations Engineer at Google, currently working in the Open Source Programs Office. My journey to Google was a little unconventional. I spent a decade as a chef in Chicago before trading in my apron for a career in tech. I started out in robotics and machine learning and even have a few publications to my name. For the last nine years, I've been at Google, where I've worked on the infrastructure and frameworks that power Google Play Services. When I'm not at my keyboard, you can find me attempting to play the ukulele, making questionable pottery, and spending time with my two cats