WEBVTT

00:00.160 --> 00:03.760
Let's discuss what we're going to be building in this section.

00:04.120 --> 00:09.240
So we're going to be diving into the fascinating world of reflection agents.

00:09.960 --> 00:16.480
And reflection agents are powerful tools that are used to enhance the quality and success rate of AI

00:16.520 --> 00:17.240
systems.

00:17.680 --> 00:23.680
They prompt an LLM to reflect on its past actions, allowing it to learn and improve over time.

00:24.040 --> 00:26.720
Now let's explore this project.

00:26.760 --> 00:28.000
Reflection agent.

00:28.040 --> 00:33.320
The reflection agent is going to help us revise Twitter posts, and we're going to use an example,

00:33.360 --> 00:37.040
a Twitter post I've written a while ago about LinkedIn.

00:37.240 --> 00:42.960
And the reflection agent is going to help us iterate and make our tweet better.

00:43.280 --> 00:50.480
It's going to take the original tweet, and it's going to give us critique with a reflection step.

00:50.680 --> 00:55.000
It's going to give us this feedback and criticize our Twitter post.

00:55.040 --> 01:00.800
Then we're going to take this feedback and feed it back to an LLM again and ask it to revise it.

01:00.800 --> 01:02.640
So we have revision number one.

01:02.680 --> 01:03.800
After the feedback.

01:04.040 --> 01:09.720
Then we're going to continue and iterate through this process again and again and again until we finally

01:09.720 --> 01:14.040
get a Twitter post that is decent and hopefully would go viral.

01:14.880 --> 01:17.280
Now, how long is this going to take us?

01:17.720 --> 01:23.040
Not much, because it's going to be less than 100 lines of code, because line graph is going to do

01:23.040 --> 01:24.640
a lot of heavy lifting for us.