The domain of Generative AI has exploded in the past few years. The conversation is dominated by the story that major vendors push, and it is features, not knowledge, that they push.

Join me to get up to speed on the right now of this whole brave new world of Generative AI. An accessible Zero to... Hero in training.

In four segments, we aim to grasp the current state of Generative AI from a hands on perspective while keeping an independent mindset.

Segment 1: Foundations: Concepts and APIs

Together, we will look at the journey to today's models and, practically, how to interact with them using common APIs.

You will learn about the progression from pre training and post training to the models that you finally get to interact with. We will look at tokens, context, reasoning models, structured output, and why models can be remarkably capable one moment and surprisingly fragile the next.

What is actually in the model, and what is rather a PaaS offering around it? What does the model really see? What building blocks do we work with in APIs and SDKs? How much can we put into context before things get weird? And how is this landscape evolving?

Segment 2: Integrating AI

Focusing on how to integrate AI into your applications, we will look at concepts such as RAG, tool calls, MCP, and currently popular frameworks that help connect all of this.

This includes how to work with larger documents and content sources to make them accessible to AI models, what you need to do with content ad hoc at runtime or in preparation when indexing it, what vectors are actually good at, and where their limitations start to show.

Putting documents into a vector database is not quite RAG yet. We will look at chunking, retrieval, reranking, context construction, and what happens when the supposedly relevant information is not actually all that relevant.

We will also look at what a "Copilot" really is and demystify how these systems are made. Along the way, we will talk about evaluation, testing, prompt injection, and what changes once an AI feature has to work more than once in a demo.

Segment 3: The world of agents

Starting with very simple agents, we will look at how to effectively leverage AI in an agentic world, meaning how to have agents do work for you, all the way up to deploying larger teams of agents on complex tasks.

What makes an agent an agent in the first place? We will start with the simplest possible loop, then add tools, state, memory, planning, permissions, and eventually other agents.

We will look at where agents are actually useful, where a simpler workflow may be the better idea, and what changes once a model is allowed to take actions rather than merely generate an answer.

This segment is intentionally a little vague because this space is moving ridiculously fast. Expect to walk away with an understanding of useful tools and patterns in an agentic world and how to integrate agentic concepts into your own work.

Segment 4: Local AI, media content, and AI beyond the Western corporate world

There are plenty of reasons why you may want or need to run AI locally on your own devices. Here, let's talk about how that is done.

Starting with the "easy button" of tools such as Ollama, LM Studio, Azure AI Foundry Local, and various other alternatives, we aim to integrate those with our own applications and then peek below the easy buttons as well.

Why are there suddenly six versions of what appears to be the same model? What is quantization actually doing? What can you realistically run on your laptop, workstation, or whatever GPU you happen to have available?

From there, we enter the wild model zoo that suddenly becomes available once we go beyond the big cloud vendors.

We will generate images, video, speech, lip sync, and more. We will also look at multimodal models that can understand different kinds of input, models coming from Chinese AI labs, and how you can mix and match capabilities from very different models and ecosystems into interesting workflows.

The goal across both days is not to memorize the latest collection of product names. It is to understand the pieces well enough that when the next model, framework, agent platform, or miracle button appears, you can work out what it actually is, what it is good for, and whether you need it at all.