Let me talk about some other phenomena that have happened over the last few years.
One of them has been the rise and fall, perhaps of a new type of job called the Prompt engineer, someone
who specializes in knowing how to craft the right kind of prompts to get the best outcomes from llms.
At one point, this role commanded a $500,000 salary and was hot in demand.
It does seem to have fallen downwards a bit now.
The demand is less, partly because knowing how to prompt well has become ubiquitous.
There's so much content now about the right ways to go about prompting, and also because there are
now tools that will actually create a prompt for you.
Anthropic, in fact, has has one of those tools.
So it is now something that has become relatively common.
Another another phenomenon was the custom gpts.
OpenAI has a GPT store that was incredibly popular for a while.
It's become a little bit saturated at this point.
I think people became fatigued of building custom GPT, gpts, but it's still there.
The GPT store is still reasonably popular, and you can go there to experiment with different kinds
of tuned gpts.
Then of course, still very important was the emergence of co-pilots ways in which a human and an LLM
could collaborate together.
Famously, Microsoft Copilot, I think, was perhaps the first one that really took the world by storm.
GitHub copilot of course, and there are many more co-pilots that are being embedded into a lot of applications.
And in a way, we sort of saw that with canvas a moment ago.
And then the new hot trend right now is all about agent ization, about using Agentic AI, which is
where multiple llms collaborate to solve a problem.
A more complex problem is broken down into smaller steps or smaller tasks, and then particularly tuned
llms are used to tackle each of those steps, perhaps also with an LM responsible for planning and deciding
which LM is doing what.
Also, with a concept of memory, that there's some kind of persistent information that lasts, that
can be exchanged between the LMS and a sense of autonomy, that the LMS don't just exist for the purposes
of a chat interface with a human, but they have a sort of time horizon that that spans multiple chats,
potentially.
So that sense of autonomy and memory and being able to plan tasks and divide tasks down, those are
all some of the core tenants of Agentic AI.
And we'll be coming back to this a few times during the course, but in particular at the end of the
course in week eight, we will build a full Agentic AI solution, as I might have said, a time or two,
but it's really great and it will have eight, seven, seven.
I don't get overexcited.
There are seven agents that will collaborate as part of what we will build at the end.