Introduction to Generative AI with Go by Daniel Whitenack

Welcome to the Introduction to Generative AI with Go Webinar!
Engineers are scrambling to figure out what they can (or should) do with this latest wave of generative AI technologies, and organizations are struggling to bring the technology into enterprise environments. Leaving this webinar, you will be equipped with the practical, hands-on expertise to start integrating generative AI in your Go applications.

You will learn the essential AI engineering skills of prompting, data augmentation, chaining, etc., and you will get hands-on with the latest generative AI models (Mistral, Llama 3, deepseek, etc.).

During the 1- hour we will cover:
- Introductions, Context
- Introduction to Modern/Generative AI
- Hands-on Interactions with LLMs
- Common AI-driven Workflows (including RAG), Data Integration
- Hands-on Examples
- Enterprise AI Concerns
- Summary, Conclusion
- Q&A

About the Instructor:
Daniel Whitenack (aka Data Dan) is a Ph.D. trained data scientist and founder of Prediction Guard. He has more than ten years of experience developing and deploying machine learning models at scale, and he has built data teams at two startups and an international NGO with 4000+ staff. Daniel co-hosts the Practical AI podcast, has spoken at conferences around the world (ODSC, Applied Machine Learning Days, O’Reilly AI, QCon AI, GopherCon, KubeCon, and more), and occasionally teaches data science/analytics at Purdue University.

Links from the Webinar:

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- Ardan Labs Information -

Other Links:

[GoWithAndy-cptz]

Thank you Daniel! it was extremely interesting for me.Cheers!

[TheReequiem]

From experience in building a RAG system in production, I've had to pipe to python for chunking, I love Golang, don't get me wrong but hell, how is it possible that there are so little text extraction libraries out there; there are seemingly not that many "mature" libraries out there for the large spectrum of file types that exist. Maybe Gophers love to build their own in-house solution every time.