Understanding AI Your First Steps into the AI Brainroom

Hey there, fellow tech explorer!

If you're anything like me, you've probably spent countless hours chatting away with ChatGPT, Google Gemini, or Claude. These incredibly popular AI tools, built on what we call Large Language Models (LLMs), have completely transformed how we interact with information, effortlessly drafting emails or summarising complex articles. They're truly "fantastic at generating and editing text".

But here's a little secret: these powerful LLMs, in their purest form, are somewhat like incredibly knowledgeable, but a tad passive, library assistants. They're trained on "vast amounts of data" like Wikipedia and Google Books, enabling them to generate responses based on learned patterns. However, they have a "limited knowledge of proprietary information" – they won't know when your next coffee chat is, or the real-time price of a flight ticket, because they don't have access to your personal calendar or current external systems. They simply "wait for our prompt and then respond".

Think of it this way:

you give them an input, and they give you an output, like a brilliant, reactive echo chamber. This is the essence of Generative AI: it excels at creating new content – text, images, video, code – in response to your request. It's a phenomenal tool for content creation, data analysis, and even personalising experiences.

So, what happens when we want them to do more than just echo?

What if we want them to do something in the real world, beyond just generating text

That's where things get exciting, and we move beyond the basic chatbot.

Stay tuned!