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How To Get Free Traffic From ChatGPT in 2025 (AIO Course)

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Okay, so here I am in Chad GPT and if I

ask it like this question, what's the

best course on SAS on WordPress? You

will see WordPress SAS 2.0 my website

learn with Hassan will pop up in the

first result. You can see this is my

website and the source chpt.

So I'm getting free traffic directly

from Chad GPT for a prompt like this

one. Let's copy it. Open code enter

again you will see I'm the first result

on load go to perplexity again the same

prompt and you will see this is my

course on top of perplexity so my

website my course is ranking on top of

AI models in this video I will share

with you seven techniques to rank your

website on top of AI models like chaptt

and code and more importantly I will

share with you a free system I built so

you can track your performance your

website mentions inside AI models. If

you are ready, let's get started. Okay,

before we start and in like 30 seconds,

let's recap together how was organic

traffic before AI in 2023 and before. So

we have Google and we go and search for

keywords for example WordPress SAS

course. So this is a keyword that people

may search for and then you rank your

website on top of Google to get traffic

directly from Google organic free

traffic. So we used to do keyword

research. We use keyword research tools

like this one. I search for keywords. I

see the search volume and then I go here

to my website and optimize the page.

What we call onpage SEO and off-page

SEO. and you start optimizing to rank on

Google and more importantly we used to

track performance with Google search

console so you can see how many clicks

you are getting how many impressions and

so on so this is traditional SEO is it

in my opinion of course not and I still

get a lot of traffic from SEO today and

I believe it's still so important but

beside SEO today we have something new

AIO or AI optimization to get traffic

directly from AI models like chat, GPT

and other models. So as I mentioned in

SEO we use Google search console so we

can track our website performance or

search engines and track which keywords

are getting impressions and clicks. So

for AI we need something similar. We

need to track our website mentions on

chip and AI models. For example, I want

to know as a website owner that this

query here that this prompt is

generating a response that's mentioning

my website and so on. So I can track my

website performance on AI models. So, I

did some research to find some tools

that can track performance on AI models

and I found things like Almrefs starting

$79 a month. Another tool is called

keyword.com. Other one is called SE

Ranking starting with $95 a month. We

have AHF starting 129. We have peak or

peace, I don't know, 199 or 89 a month.

We have first answer starting for $39 a

month for only 10 monitored prompts and

so on. So we have several tools and

honestly I didn't test any of these

tools and they might be great. But in

terms of pricing I felt somehow

expensive especially for people starting

new and they have tight budget. So I

decided to create and build an

alternative. So the first step I will

show you how to set up this system so

you can track your performance on AI

models. Then I will share with you the

seven tactics to rank your website

inside AI models. In this way we have

like a full course on AI optimization

for free organic traffic from AI. So to

build this system this tracking system I

used make. I chose make first because

it's no code. It's super simple and it's

totally free to get started. So if you

are new here, make is a no code tool.

Allows you to build any automation you

want, any system you want in a super

simple way and I partner with make to

give you one month pro access totally

for free. Just sign up using the link in

the description and follow up with me.

In my case, let's log into my account.

And when you are here in the dashboard,

just click on scenarios and click on

create a new scenario. Now, our tracking

system consists of three scenarios. One

is for tracking that you'll see right

now. And the second two is to build the

user interface like this one, this

dashboard. You can see I click on this

button, we get this simple report to

track everything with this clean UI. So

let's go back to our scenario. And to

make things simple for you, I will share

with you in the description a link to

the JSON file that you can use to import

the scenario directly. So just click on

these three dots here and input the

blueprint. Choose file and then simply

select the blueprint, save, and boom,

you will get the scenario. Now, forget

about this part here. I'll talk about it

later. Our main flow is this one here.

Okay, it's super simple as you will see

right now. What's going on here simply

is the first module, the first step is

just reading the queries or the prompts

we want to track on AI. So if you go

here to make, we have something called

data store which is built in in make and

this is what makes make great. It has

all the features you want directly

inside without any external services. So

you can see here I created these four

databases or data stores or tables that

connects with our scenario. The idea is

super simple as you will see right now.

So we have Alam queries. If you browse

this one you will see I added four

queries like the four prompts I want to

track. Again if you go to charg here. So

this is our prompt. It's like keywords

in the old SEO. Today we have prompts or

what we call LM queries. So here you add

your queries and the second one is for

keywords. So here these are the keywords

or what you want to track. In my case I

want to track my website mentions. So I

added lemouth hassan and lethassan.com.

So these are the main two keywords I

want to track. And here we have two

other tables. One for keyword tracking.

So here you will see I'm tracking the

keywords when my website is mentioned

and I have the other table for brand

mentions. So this system not only tracks

your website, it also tracks your

competitors and this really game

changer. So you can see your competitors

where are ranking on AI. So also you can

optimize and outrank them. So we have

four data stores and it's super simple

to create such data store. If you click

on edit, you will see I have simply here

a query. That's all. Just click add data

store and you give it a name. For

example, lm queries. And here you click

on add and add an item. And simply add

query. That's it. And you click on save.

And you get this data store and so on.

For tracking, if you open this, we have

query again. We have keyword and we have

found which is a boolean value true or

false and we have the date and then the

lm if you are targeting multiple AI

models like shajyp and code and so on.

So we can track on which AI we are

monitoring and then save. The same for

keywords. It's very simple structure. We

have here a keyword only. And here we

have brand mentions. You will see we

have brand query date URL and that's it.

Four fields. It's super simple. You just

need to copy and paste these to build

your four data stores. So when you build

them you will see here the first one is

simply connected to LM queries. So we

are reading the prompts that we want to

track. The second step is simply an

iterator which loops and read one by

one. Then we go and call charg GPT. This

one please focus here. What I'm doing

simply is I'm calling charg with GPT40

search preview. So here please one

important point if you go to charge

again let's repeat the session new chat

copy the prompt please focus here you

will see that chargept is searching the

web you see search the web before

generating the answer so here we are

using a tool to search then it responds

the same here if you want to mimic this

behavior you need to use a search

preview you can also use the default

model without web search to track your

mentions on LMS without search. So this

is other model if you want we have model

with search and we have a model without

search. In my case I'm using the search

preview to mimic the same behavior on

chipt then I will create these two

openai modules to extract the data in a

formatted structure in JSON. You see

here I tell it return a JSON brands and

URL. So it's simple and it's easy for me

to read and save in a database. I will

show you now an example to understand

what's happening. This one here just

gets a JSON and formatted well and

converts to structured data. So these

are just for structuring the response we

get from here. Then when we get all the

mentions, all the brand mentions, we are

going to again iterate loop one by one

and save to our second data store. You

see here to brand mentions. So here we

are extracting all the brand mentions

and saving them inside the data store.

Then what I do is I want to check if my

keywords if my website is mentioned in

the responses. So you can see here I get

my keywords from the keywords table. So

I loop and here you can see we have a

filter. I check if the messages coming

from Chad GPT contains my value or my

keyword and I save it inside the

keywords tracking database. So again to

sum up in the first step you are going

to get all the prompts. In the first

part here we are going to extract all

the brand mentions every company

mentioned from the responses and save

them. Then this second part these three

modules will check only for my keywords

filter out the responses containing my

website and save it in this data store.

Let me run this system once inside make

to help you understand what's going on.

So I will run the first step is it will

get the queries. It will start looping

one by one. So this is the first

iteration. We are calling chajp with

search and extracting the responses. You

can see here we have the response

message content. You see this is the

chip response. You can see my website is

here. So this is the response from

charg. The second step is we are

extracting this response. You see here

choices we are just transforming the

response into this JSON. You see

structured JSON then this one is used to

ensure we have will turn it into

structured response that make understand

you see here now we have one the first

response second response third response

and so on. So we have now the full

results the full brand mentions

structured in JSON format. Then this we

have the iterator. Then it saves all the

records to data store. And here we are

filtering my mentions. You can see the

filter. It checks if I am mentioned and

if I am mentioned it stores the mentions

in data store. So if you go back here

and open for example brand mentions you

will see all the records that we got. We

have now 180 records, 100 brand mentions

saved inside this data store. So this is

the first scenario you want to import.

And one note, you need to configure all

these data stores. This one with LM

brand mentions and this one with LM

keywords and this one with LM keyword

tracking. And finally, don't forget to

set up your OpenAI key here. To add a

connection, you just add and enter your

API key. So you have access to open AI.

So the first scenario that tracks and

collects data is ready. You just need to

run it. You can schedule it for example

once a day or once a week. It's up to

you. And now we have the data saved. We

just need a way to read and view in this

simple dashboard. So we can track it

with a simple UI. So the second step is

to create these second two systems. This

one which are very basic. This one is to

read data from our keyword tracking and

return it as JSON here as an array. So

again I will leave the scenarios. You

can just import this is very simple. We

have a web hook which acts like a URL.

Let me show you this. If I open it gives

you a URL. And if I copy it and go and

open my browser here, paste, enter, you

will see I get the full data in JSON

format. You see in the browser. So this

is just like a system that allows me to

read data from the database inside make.

This is the first one. And the second

system that reads the brand mentions.

Same way we have a web hook or called an

API endpoint. A URL that you get and use

to read the full data in JSON format. Of

course, you are not going to read such

data in the browser. This is why I

created this simple interface for you.

You just need to enter your make URL

here. Click on fetch data and then it

will format it like the query, your

mention. You can see the keyword in

which language model and by date the

second keyword and so on. The same for

brand tracking. You just fetch data. It

will show you all your competitors all

the web pages ranking for the same

keyword on Chipd. You can see we have

now different websites. I can discover

my competitors, see who is building

something like my course and so on. And

to make things simple for you, you just

to get this simple page. And if you want

to get the source code, I will leave it

so you can run it locally if you want or

maybe optimize it and host it and have

your own system to track your mentions

on language models. So again, we have

three systems or three scenarios. one

for tracking, one for reading data brand

mentions and one for reading data from

keywords and this is the UI. So you have

the full system to track your

performance on AI models. Now before I

move on to the seven tactics to rank

your website after we are monitoring the

performance, I want to mention that you

can extend the system to track on any AI

you want. For example, you just need to

go here and add a router and then simply

for example, you see this route here.

You can instead instead of charg for

example anthropic load and clone the

same path here. For example, you can add

a route and you can search here for perplexity

perplexity

and you can add perplexity AI or simply

Gemini and so on. So you can track on

multiple AI systems. Just make sure here

when you want to save data to choose the

LLM, in my case it's GPD40, then you

just change it for Gemini or code or

whatever. So this way in the UI you can

see different LMS here in this section.

And what's nice about make they

introduced this new grid system where

you can see the full scenarios in this

awesome Iird view. So you can track

exactly what's going on inside your

systems and this is especially helpful

if you want to scale the system and even

build a backend for your micros with

make. Yes, with no code like make you

can build a full backend to your Micros

tools. You can see you can track

everything and switch to 2D. You can

turn light mode. You will get

notifications if anything is happening

and track your transfer view and spot

any problems easily inside your account

or your systems. Not only that, on my

website, I published this case study or

this stress test I made with make to

check if it can handle 1,000 requests

per minute simulating a realworld

microsass traffic. And you can see here,

you can read this blog and in the

results, you can see I got 100% success

rate in all tests. So we can say

comfortably make can handle 1,000

requests per minute and you can build a

full backend for your tools or microsass

with no code and make and the grid

system will definitely help you when you

scale your scenarios with make. One last

note I want to mention that this system

I built here is somehow simplified to

help you understand the concept. You can

add more fields you want to track for

example the history. So you can build a

chart. You can add more fields and track

more data within the system. You can

expand, optimize as you like and maybe

you can turn this into your own tool or

your own micros for LM tracking. Okay,

now it's time to see the seven tactics

to rank your website and put your

website inside AI responses putting it

in front of millions of users using AI

models every single day. But a small

disclaimer to be transparent. What I'm

about to share with you is not something

100% guaranteed simply because this

field this studies are still new and

there are limited test and research

around this topic and I myself am still

testing experimenting different tactics

to optimize my websites for AI and you

will see right now some examples I will

show you from my websites. I'm still

building and experimenting right now,

but I tried my best and did the

research, experiments, and tests to

share with you the most important

tactics and tips to rank your website on

AI and get free traffic from AI. So,

let's get started with tactic number

one, adding statistics, numbers, and

proof to your content. LLMs or AI models

are trained to give priority to content

with verifiable information and factual

responses when generating a response to

a prompt. So try your best whenever you

write a blog, a page, anything to

mention numbers and proof. For example,

maybe not the best example, but

something that we can rely on. This

test, this one, the stress test. Here

I'm sharing something I did a test

verified with numbers and I tried this

by the way while I was doing and testing

this I asked Chajip for something

related and you can see my website here

even my medium blog appeared in Chajip

results. So, it used my content to

respond to my prompt. And I think this

is because I have a real test, something

verifiable in my blog that LMS can rely

on to respond. So, this is the idea is

to write or create content when LM read

it, it can see verifiable information

and build what we call a trust

confidence score. This will help

consider your website as a source of

information when generating responses.

Tactic number two, engaging on Reddit,

forums and Kora. LMS or AI models are

trained on millions of Reddit

conversations and threads and Kora

questions and answers and user generated

content because it's considered or often

seen as authentic and unbiased content.

So language models and AI companies deal

with user content and discussions as a

source of authenticity. This is why you

should consider joining Reddit, forums,

Kora and communities. So you can give

trust signals to language models using

these sources. Also you can build your

own community, your own forum on your

website. So user engages, ask questions,

answers on your website and this may

give other trust signal to AI that your

website have authentic conversations and

will deal with it as a trusted source.

For example, on my website you see I

have a community, I have forums where

people engage, ask questions and discuss

topics related to marketing and digital

products. Tactic number three is fact

optimization or frequently asked

questions optimization. So before AI

usually people used to search Google for

keywords for example WordPress SAS like

a keyword but today people are more into

questions asking full questions like

what is the best online course to build

SAS on WordPress. So people will ask

full questions and this is where it

comes answering these questions on your

website. So for example in this blog

post here if you go down you will see I

have a fact sections answering common

questions people might ask about the

topic. So this way when chip or AI find

the exact same question on my website

with the answer combined with the trust

signals we mentioned before it may use

my response my website to answer the

user question. So you can search Google

and use tools to find questions people

are asking and then you can answer them

inside your blog posts and your web

pages. Even if you are building tools

for example like in my case toolbox I'm

building tools here make sure to add a

fact section like this one to answer

common questions about such tools.

People may ask questions like what is

the best keyword research tool and if I

answer this question here Jajipity may

use my answer to reference my web page

which which includes my tool and so on.

Tactic number four is what we call

creating comparison table or structured

data. These tables or comparison content

will help LM and AI models answer

questions like what is the best X or

what is the best product and so on. For

example, just yesterday I was working on

this new comparison page especially for

testing this AI traffic strategy. So you

can see I'm building this VPS companies

page on my website selfhostchool.com

and here I'm adding different VPS

providers with comparisons getting data

trusted data collecting real data from

the web and building this structured

table on my website hoping that whenever

someone asks for a VPS comparison or

what is the best VPS or so AI can use my

data my collected data my structure

structured data to answer people's

question and I can get traffic to my

website from AI using this strategy. So

in your niche find what type of

comparison data you can create and you

can create these tables or content

directly on your website and somehow

simple today with WordPress and some

custom snippets if you are following my

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How To Get Free Traffic From ChatGPT in 2025 (AIO Course)