DSPy: Advanced Agents?
Following our Part I on DSPy: Advanced Prompt Engineering? We’re following up with Part II: DSPy: Advanced Agents?
In Part I of our series on DSPy, we learned to leverage DSPy as a wrapper to automate prompt engineering. We also learned a similar approach called “bootstrapped few-shot with random search” to Augment the prompts in an RAG application!
In this event, we’ll expand our understanding of DSPy for In-Context Learning and move up the Generative AI prototyping pattern abstraction layers to Agents!
According to DSPy’s author, Omar Khattab, the project “unifies prompting, fine-tuning, reasoning, and retrieval augmentation.” We can use DSPy to “express ‘any’ pipeline as clean, Pythonic control flow.”
In this event, we’ll examine how to best leverage DSPy for “reasoning” or “agentic” applications. By optimizing an agentic application, we aim to clarify the DSPy tool as a whole. Is it similar to how we used it to automate prompt engineering and augmentation?
Join us live to find out!
📚 You’ll learn:
The core concepts that extend DSPy for Agents
How DSPy can be used to optimize your Agentic reasoning application
Where you should think about fitting DSPy into your workflow
🤓 Who should attend the event:
Aspiring AI Engineers who want to understand the latest prompting (ahem, programming) methods
AI Engineering leaders interested in optimizing production agentic applications
Speakers:
Dr. Greg” Loughnane is the Co-Founder & CEO of AI Makerspace, where he is an instructor for their AI Engineering Bootcamp. Since 2021, he has built and led industry-leading Machine Learning education programs. Previously, he worked as an AI product manager, a university professor teaching AI, an AI consultant and startup advisor, and an ML researcher. He loves trail running and is based in Dayton, Ohio.
Chris “The Wiz” Alexiuk is the Co-Founder & CTO at AI Makerspace, where he is an instructor for their AI Engineering Bootcamp. During the day, he is also a Developer Advocate at NVIDIA. Previously, he was a Founding Machine Learning Engineer, Data Scientist, and ML curriculum developer and instructor. He’s a YouTube content creator YouTube who’s motto is “Build, build, build!” He loves Dungeons & Dragons and is based in Toronto, Canada.
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