This is just a quick video to let you know that we've added in some additional resources.
If you're interested in learning about different types of cognitive agent architectures, we've covered
the simple mixture of experts aggregation model, but you might be interested in different types of
agent workflows.
One that's quite interesting, for example, is hierarchical agent teams where you have different levels
of agents.
So you might have a research team, you know, a document offering team, a supervisor that supervises
everything, and then various different types of agents.
below.
You've also got things like the plan and execute model, and this has been typically shown for different
types of ones such as baby AGI, and also is very similar to a react style agent, where you can think
of it as one step at a time, where you have a, you know, a request you plan, you generate some tasks
that goes to a single task agent that loops to solve that task, updates those tasks, then maybe have
some replanning.
The response gets sent back to the user.
And so this is basically quite a useful one.
This you know, this is often been the case using this inside of things like code generation and also
advanced task planning.
You've also got more simple architectural patterns that are quite useful to know about.
So for example reflection is a really easy way of basically asking the large language model to produce
an output, and then after it's produced an output to reflect on what is the quality of that output.
And you can see here we have this user request which goes in and generates.
And then it will make an initial response.
We reflect on that initial response with some sort of critique or evaluation criteria.
And then we will then use that critique to then further generate and change the underlying asset.
And this can be quite useful.
For instance, if you're making like a blog post or a social media post, you might want to ask the
LLM to critique itself or self reflect and say, you know, have you covered these things and making
sure that the LLM always incorporates them?
The one thing you want to note here is the number of times you do reflect will obviously increase the
cost.
So don't tell it to like reflect 100 times, but maybe like 1 or 2 times could potentially improve the
outputs of your LMS.
So we've left all these notebooks there inside of the GitHub repository.
And we'll also give you a link on this lesson.
So you can easily dive in and look at these notebooks in further detail.