The terminology trap
Everyone calls it AI. Marketing materials promise artificial intelligence that thinks, reasons, and understands. News articles warn about AI taking over jobs. Product announcements tout AI-powered features. But here's the thing: we're not really talking about artificial intelligence in the broad sense.
What we're actually using are Large Language Models, or LLMs for short. These are computer programs trained on massive amounts of text to predict what words should come next. That's it. No consciousness, no general intelligence, no understanding in the human sense.
What's an LLM?
A Large Language Model is a program that learned patterns from billions of text examples. When you give it a prompt, it generates a response by predicting likely word sequences based on those patterns. Think of it as autocomplete on steroids, not a thinking machine.
So why does everyone say "AI"? Because it's shorter, catchier, and frankly, it sells better. But using the wrong term creates confusion. It sets unrealistic expectations and makes it harder to understand what these tools can and cannot do.
Throughout this course, we'll use the term LLM when we're talking about language models specifically, and AI only when discussing the broader field of artificial intelligence. Getting the terminology right is the first step to understanding how these tools actually work.
What this course covers
This course takes you from the basics to making informed decisions about using LLMs in your work. We'll start with how LLMs work under the hood, exploring the technology that powers tools like ChatGPT, Claude, and GitHub Copilot. You'll learn about neural networks, transformers, and the breakthrough that made modern LLMs possible.
Then we'll look at what LLMs can actually do. Not the marketing promises, but the real capabilities and limitations you need to know when deciding whether to use them in your projects.
We'll dive into agentic systems, which are LLMs connected to tools and given the ability to take actions. These are the coding assistants, customer support bots, and automated workflows that are changing how software gets built.
The course also covers practical concerns: what hardware you need, whether to build your own solution or use existing providers, and how to navigate the rapidly evolving ecosystem of models and tools.
Finally, we'll examine real-world implementations. What worked, what failed, and why. You'll learn from companies that successfully integrated LLMs and from those that struggled, so you can avoid common pitfalls.
Who this is for
This course is designed for developers and technical decision-makers who want to understand LLMs without the hype or the handwaving.
You don't need a background in machine learning or data science. We'll explain concepts from first principles, using analogies and clear examples instead of academic jargon.
You should be comfortable with basic programming concepts. We won't dive into specific code, but understanding how software works will help you grasp how LLMs fit into larger systems.
If you're evaluating whether to use LLMs in your product, wondering if they'll replace your job, or just trying to separate reality from marketing, this course will give you the foundation you need.
What you'll learn
By the end of this course, you'll understand how LLMs actually work. Not just "they use neural networks" but the specific architectural choices and training approaches that make them effective at generating text.
You'll know what LLMs can and cannot do. You'll be able to identify good use cases and spot situations where an LLM is the wrong tool for the job.
You'll understand agentic systems and how they differ from simple LLM calls. You'll see how agents use tools, manage context, and orchestrate complex workflows.
You'll be able to make informed decisions about infrastructure. Whether to run models locally or use cloud services, which hardware makes sense for your scale, and how to estimate costs.
You'll understand the ecosystem. Who the major players are, what open-source options exist, and how to choose the right model and provider for your constraints.
Most importantly, you'll have realistic expectations. You'll understand where LLMs excel, where they fail, and how to work with their strengths while avoiding their weaknesses.