Let's discuss what we're going to be building in this section.
So we're going to be diving into the fascinating world of reflection agents.
And reflection agents are powerful tools that are used to enhance the quality and success rate of AI
systems.
They prompt an LLM to reflect on its past actions, allowing it to learn and improve over time.
Now let's explore this project.
Reflection agent.
The reflection agent is going to help us revise Twitter posts, and we're going to use an example,
a Twitter post I've written a while ago about LinkedIn.
And the reflection agent is going to help us iterate and make our tweet better.
It's going to take the original tweet, and it's going to give us critique with a reflection step.
It's going to give us this feedback and criticize our Twitter post.
Then we're going to take this feedback and feed it back to an LLM again and ask it to revise it.
So we have revision number one.
After the feedback.
Then we're going to continue and iterate through this process again and again and again until we finally
get a Twitter post that is decent and hopefully would go viral.
Now, how long is this going to take us?
Not much, because it's going to be less than 100 lines of code, because line graph is going to do
a lot of heavy lifting for us.