And they then some time ago released a new feature, which is absolutely outstanding because it makes
our agents 3 to 5 times better.
They are more productive, accurate, and even we can save a lot of time by applying this simple feature.
And I'm talking about the think node.
So in general in this video will cover everything about it.
And also two very important use cases.
So the first one will be about increasing the chances of your agent does exactly what you want.
Because we know that for an example, when we run some prompt, sometimes it produces the response which
is just bad.
It misunderstand that.
And a lot of like more issues we have here.
Nevertheless, with this feature we can increase our chances that all right we get what we want.
And the second use case is to turn our agent to speak more like a human, not actually as an AI.
Um, so with all that being said, let's get started.
And firstly, I can show you how it works.
So how think note works.
And we've got this simple, um, a agent that is connected to OpenAI and the think node, and we trigger
this, um, agent with the message so I can open the chat.
And here I prepared some message I can paste even.
Let me show you this prompt here.
So it's nicely visible.
So a government wants to reduce car traffic in major cities by introducing a new law.
Every citizen must choose between owning a private car or using public transportation, but not both.
Ride sharing apps and taxis will be still allowed, but owning a car disqualifies you from using subsidized
public transport, and vice versa.
Your task is to evaluate the policy as a smart, long term solution.
So step by step, identify key assumptions behind the policy, predict likely outcomes and different
types of cities dense, sub, urban or rural, and list at least three unintended intended consequences
this policy might cause suggest refinements or adjustments to the policy to make it more realistic and
fair.
Conclude whether the revised policy is better than doing nothing.
So now I can copy that.
Go over to my to my na den and here just paste it inside chat.
And now we can see how it works.
So we've got our new message agent and open AI.
And actually it will use the think note to think about it.
So in the common situation we've got our agent that uses only open AI.
All right.
So we've got you know like the input and output.
So this is the entire process that goes with the LMS actually.
And other videos.
Um we've got explained how it works.
However simply it takes the input, it predicts actually the next words and it creates the response.
All right.
Based on some specific database.
Nevertheless, with the think note, it actually thinks like a real human.
So let me explain that.
Firstly we've got the input, as you can see here.
All right.
We've got the input.
So human.
A government wants to reduce car traffic.
So this is our prompt.
Then what it did instead of just going like straight forward to creating for us the response it used
the think node.
And now you can see it identifying key assumptions behind the policy.
And one limiting the ownership of private cars will significantly reduce traffic congestion in urban
areas.
Citizens will easily adapt to using public transportation if they don't have a private car.
So it just took the prompt and it thought for some time.
Again, we've got another thing actually.
Note.
So predicting likely outcomes and different types of cities.
So here we had the policy there the cities there we have listing unintended consequences.
So here as you can see like the thinking process is as we would have done it as humans.
So the think note is so so nice.
And here is actually the comparison.
Think note inside Nadhan is like the reasoning model right.
So you've got reasoning models like Deep seek and R1.
And here for an example OpenAI or one.
So these are reasoning models and it works like pretty the same.
All right.
Because we've got yeah this think node.
So we just turn our typical LLM which is this one OpenAI to just the reasoning model kind of and there.
All right let's let's preview what we have else.
Firstly we have the input three.
Um no three actually steps for thinking.
The fourth step is actually creating, you know, creating the output.
The fourth step we've got our input and that is refined for our OpenAI model.
So here what I did.
It took our, you know, data from the prompt we provided.
And here also it added there um the outputs from thinking notes.
So here we've got three actually, you know, we've got key assumptions behind the policy, um, outcomes
and different types of cities.
And here, um, consequences.
So what I did again actually took our input, thought, um, about some circumstances, updated the
prompt here.
Again, it thought for the next time.
Then again, it updated the prompt with this thought.
Again it thought, all right.
And at the end we've got the output after all of this data.
So it collected the data here and at the end like used it um in our output.
And it's absolutely outstanding because it thinks it allows actually think note allows our agent to
think.
And by this way we can avoid a lot of different mistakes in the future because it refines it, recalls
some data, it checks.
All right.
Um, if this data was correct, if I was correct or not.
Yeah.
So it works by this way.
And by the way, the think node was inspired by anthropic article.
So here they introduced the think tool enabling cloud to stop and think and complex tool use situations.
So here um, actually you've got we won't read this entire article.
However we'll yeah like take the most important parts.
So here, as we continue to enhance clouds complex problem solving abilities, we've discovered a particularly
effective approach, a think tool that creates dedicated space for structures thinking during complex
tasks.
So imagine, um, actually you are solving some complex math problem and you've got the paper and you
can like, you know, you can like type your thoughts on that.
So as you saw in this entire example what it did, again, it used the thinking process to just create
for us some data.
And afterwards it used that and our final response.
So it makes a lot of sense here.
What we can read this simple yet powerful technique, which as we'll explain below, is different from
clouds.
All right.
So we are not interested about it.
Um, here what is the thing tool?
With the thing tool, we are giving cloud ability to include an additional thinking step.
And again this is analogy for the think tool inside an addon.
So um yeah it's about it.
Um, additional thinking step complete with its own design space as part of getting to its final answer.
The thing tool is for cloud.
Once it starts generating a response, to add a step to stop and think about whether it has all of the
information it needs to move forward.
This is particularly helpful when performing long chains of tool calls or in long, multi-step conversations
with the user.
That's why here we had the case.
So, you know, we had um, yeah, very complex problem that we would need to solve.
And in these circumstances this thing tool and think node is just perfect because it breaks the problem
into smaller pieces.
It thinks it just, you know, as I said, the analogy to the paper and solving, you know, solving
very complex problem.
You just type your thoughts on paper and then you just refine your answer and do other stuff.
This makes the thing tool more suitable for cases where cloud does not have all of the information needed
to formulate its response from the user query alone, and where it needs to process external information.
Also, in this article, you've got the recommended uses for um think um tool.
It's about the cloud.
However also it's yeah you know performs when it comes to denim.
So here when to use the think tool tool output analysis when clouds needs to carefully process the output
of previous tool calls before acting and might need to backtrack in its approach.
So also if you have actually, let's say a voice agent and let's say you are, um, making appointments,
you can use this tool to actually see.
All right.
So actually think if for an example that we've got the data which is available or something else.
When you create some complex text, like a lot of different use cases, we won't cover that in this
video.
However, um, you know, after you know how to use it, you can implement that in many, many cases.
So here, policy heavy environments when cloud needs to follow detailed guidance and verify compliance.
Here decision making.
So also decision making is a great use case because it it needs to like think a lot break the problem
into smaller pieces.
So when each action builds on previous ones and mistakes are costly.
And that's why I said at the beginning that we, um, we created like this, um, sample is just actually
it allows our agent to be more accurate, to be more precise, and to make just less mistakes.
Because if it can think all right, if it thinks it's just better, it's simpler, it's it's better,
it's more accurate.
So it's very important implementation best practices.
So here you've got strategic prompting here.
Um, complex guidance and system prompts.
And they're even for what you don't need to use think tool.
So for sure if you have like simple cases right, you need to create some piece of the text that doesn't
require like thinking like, you know, like something very sophisticated.
Don't use this tool.
Nevertheless, in most cases and in most automations, because you've got, um, for sure, you've got,
um, these automations that are very advanced, you would like to use think tool.
It makes your agents just better, more effective, like on the steroids.
Now, let me show you how we can add the think tool and to your agent.
And it's simple as I'm clicking on this plus um, under the tool and here just selecting the think so.
So just search for the think tool.
And here you've got it actually it has the prompt.
So we can actually um adjust the behavior of the thinking note.
However I will show you that in a while.
And now it will be nicely visible on this example, because I will show you how to create an agent that
speaks more naturally, like a human, not a robot, which is nice for a lot of circumstances.
For example, if you create a piece of text, article, blog post, or even social media content.
All right, so here inside the AI agent instructions, inside the prompt I've got here, I'm actually
let me show you overview.
You're a helpful assistant.
And the instructions when you create any piece of written content you use a think node to make it human
like.
And then I provided the variable.
So they're also inside the think node.
As I said we can change the behavior of this node.
So there I've cut this very long um actually prompt.
And then you can actually copy from our file that will be in the resources of this material.
So here you've got think node instructions and we've got the overview.
So this tool should reflect on a message from a user and respond to it in a more natural, clear and
human way.
It should feel like a smart assistant taking a moment to think before saying something.
And here we've got the personality and tone, human like language examples.
All right.
So what to use and suggest that vocabulary.
And so there.
For an example instead of slightly a bit and seems fix and words for example to avoid.
All right.
And now you will see that in action.
So I've got here let's open the chat and let's see what we have.
I've got some article prepared.
Um so let me copy that.
Let's paste it here.
And in general also let me paste it here.
So I will show you what we have in the current digital landscape.
It is of utmost importance of enterprises to commence the implementation of artificial intelligence
methodologies to facilitate improve, improved operation throughput.
Organizations that endeavor to utilize cutting edge systems.
So you can see this piece of the content is very rough.
I can say to read or even it's very robotic.
However, with, um, you know, with this agent, we've got the output.
So here we can go right there and click on locks.
Um, actually.
All right.
You see that?
So here we've got the instructions.
Think so in the current it took actually some pieces of the content and it refined that.
All right.
So let's see.
Yeah.
And here we've got our output.
In today's digital world it's really important for businesses to start using AI to boost how they operate.
Companies that jump on the new tech are often the ones that see big improvements in how well they do.
So it's much more human like.
It's just 100% better because the market is changing.
Companies need to take a good look at their old systems to see if moving to an AI setup makes sense.
So I wouldn't even.
I'm distinguished Distinguish like this text, which is written by eye from a human.
All right.
And that wrote it.
So it's simple as that.
Actually you can use also this think note to make the text human like.
And I think it's useful for especially for written content when you want to get, you know, like the
best piece of text without any, you know, and the weird elements and even, um, all of us know like
AI for now, llms are not that perfect.
Sometimes it produces for us bad results, especially if we are using that a lot and we can occur the
situations where we just get a bad response.
I can say one per five responses and it's very frustrating and by this way you can avoid it.
So you don't need to refine something.
You can just more trust your AI agent because as I said at the beginning, it's more accurate, just
better.
And now even you can type, you know, like create better content.
Um, with the thinking note.