---
title: 'How to Build AI Chatbots: Full Guide from Beginner to Pro (Latest Update)'
source: 'https://youtube.com/watch?v=SWP3k-24jT4'
video_id: 'SWP3k-24jT4'
date: 2026-06-15
duration_sec: 0
---

# How to Build AI Chatbots: Full Guide from Beginner to Pro (Latest Update)

> Source: [How to Build AI Chatbots: Full Guide from Beginner to Pro (Latest Update)](https://youtube.com/watch?v=SWP3k-24jT4)

## Summary

This comprehensive guide covers everything needed to master AI chatbots, from understanding how they work to building and selling them to businesses. It includes theoretical foundations, prompt engineering techniques, practical tutorials using no-code and custom-code approaches, and insights on starting an AI automation agency.

### Key Points

- **Introduction and Video Overview** [00:00] — Bogdan introduces the video as the most comprehensive guide on mastering AI chatbots, covering theory, building, and selling to businesses.
- **AI Business Potential** [02:20] — The AI market is estimated to hit $1 trillion in six years, and businesses are rapidly adopting AI. The timing is right to start building and selling AI chatbots.
- **Types of Chatbots** [07:37] — Two types: old-school rule-based (limited, manual) and AI-powered using large language models (LLMs) for understanding and answering queries.
- **Three Main Elements of AI Chatbots** [08:19] — User prompt, knowledge base, and LLM. The chatbot searches its knowledge base, processes the prompt, and generates an answer.
- **Token Limitations** [08:42] — LLMs have token limits (e.g., GPT-3.5: 4,096 tokens, GPT-4o: 128,000 tokens). Tokens are used for processing user query, pulling info from knowledge base, and generating response.
- **Chunking to Overcome Token Limits** [09:48] — Knowledge base is split into chunks; AI picks relevant chunks to answer the user's prompt, reducing token usage.
- **Embeddings for Relevance** [10:52] — Embeddings convert text into numerical vectors capturing semantic meaning. Similar meanings are placed close together, allowing the system to retrieve relevant chunks.
- **Prompt Engineering Overview** [13:44] — Effective prompting is essential for cost and efficiency. Two types: conversational (for small tasks) and single-shot (for scalable solutions).
- **Components of a Good Prompt** [14:48] — Role, task, specifics, context, examples, and notes. Each component is supported by a prompt technique (e.g., role prompting, chain-of-thought, emotion prompt, few-shot).
- **Role Prompting** [16:17] — Assigning a specific role to the LLM can increase output accuracy by up to 25%. Include a complimentary description of abilities.
- **Chain of Thought Prompting** [17:14] — Instructing the model to think step-by-step can boost accuracy by up to 90% for complex tasks.
- **Emotion Prompt** [18:58] — Adding emotional stimuli (e.g., 'your role is vital') can boost accuracy by up to 115% for complex tasks.
- **Context in Prompting** [20:00] — Explain the chatbot's role in the business context and emphasize its importance with emotional appeal.
- **Few-Shot Prompting** [21:51] — Providing examples can increase accuracy by up to 57%. Use 4-5 examples on average to balance performance and cost.
- **Notes Section and Loss in the Middle Effect** [23:47] — Include 'I don't know' to prevent hallucinations, allow step-by-step thinking, and be encouraging. Important info should be at start or end of prompt to avoid being overlooked.
- **Additional Prompting Tips** [25:37] — Implement all techniques for up to 300% performance boost. Keep prompt short for cost. Use cheaper models with good prompt engineering. Set temperature to 0 for deterministic results.
- **Use Cases and Benefits** [27:40] — Benefits: 24/7 availability, multi-language, cost savings, personalized upsells, data analytics. Advanced: lead qualification, customized sales funnels, product recommendation with affiliate links.
- **Toolkit Overview** [30:16] — Prototyping: Chatbase, Dante AI. Chatbot Builders: Voiceflow, Botpress. Integration: Make.com, Zapier. Custom Code: Node.js, AWS Lambda.
- **Chatbase Tutorial** [31:21] — Create a basic customer service chatbot in minutes using Chatbase: add data sources (files, website, Notion), customize instructions, embed on website.
- **Dante AI Tutorial** [34:29] — Dante AI allows using Google Drive/Sheets as knowledge base, lead generation forms, and meeting booking. Easy to set up with preconfigured templates.
- **Voiceflow Tutorial** [40:41] — Build a more advanced chatbot with product recommendation using Voiceflow, Google Sheets, and Make.com. Step-by-step guide provided.
- **Custom Code Chatbots** [60:00] — Custom code offers more flexibility and cost savings. Examples using Gemini, Claude, and GPT-4o with image recognition capabilities.
- **LLM Comparison** [70:10] — Comparison of Gemini 1.5 Pro, Claude Sonnet 3.5, and GPT-4o: context windows, speed, costs. GPT-4o is most expensive but widely used. Testing is key.
- **Claude and Gemini Code Examples** [76:58] — Simple code structures for building chatbots with Claude and Gemini, including image recognition. Code templates provided.
- **Advanced Custom Code Chatbot Demo** [79:31] — A comprehensive chatbot combining customer service, product recommendations, and image recognition using GPT-4o. Code explained.
- **Conclusion and Next Steps** [86:29] — Viewers now have a full understanding of building AI chatbots. Encouraged to join the AI Fellowship program for deeper learning and to start generating leads through content.

### Conclusion

Mastering AI chatbots requires understanding the underlying technology, prompt engineering, and practical building skills. With the right tools and knowledge, you can create valuable solutions for businesses and capitalize on the growing AI market.

## Transcript

hi welcome back to the channel my name
is Bogdan and in this video I'll provide
you with the most comprehensive piece of
content on how to master AI chatbots if
you watch this video from start to
finish you'll be able to clearly
understand how AI chatbots work how to
build them and which kinds of chatbots
you can sell to businesses there is a
great tutorial on AI chatbots published
by leam Motley it covers a lot of Basics
so if you haven't checked it out yet
feel free to do so but it was published
almost a year year ago and since then
we've had a lot of updates Liam and I
actually discussed this recently how
rapid the technology advancement is I
mean in the last 10 months we've seen
more capable GPT models higher token
limits new tools Vision capabilities
once GPT 40 is fully released will have
audio input and output and all of these
updates obviously create new use cases
for businesses so long story short this
video is going to be an updated version
of a full guide on AI chatbots my team
and I worked on this video for over a
month we could have made it a paid
course but we decided to offer it for
free well not exactly for free you pay
for it you pay for it with your
attention which is the currency of today
right so make sure your investment pays
off watch this video as many times as
you need but understand the information
here and act on it remember knowledge
alone doesn't equal results your actions
do and for full transparency my business
interest here is clear I've been
building IT solutions for The Last 5
Years with my current CTO we build a SAS
product a Marketplace and now we run an
AI automation agency you can check it
out at boss. agency what we essentially
do is build these AI chat Bots voice
Bots and automation Solutions and we
sell them to various businesses we have
a team of developers we are fully
equipped to take on more complex
Solutions and we are not me specific so
the more leads we get the better if you
watch this video and find it valuable
YouTube algorithm will push it more I'll
get more attention and more leads for my
business I hope that makes sense to you
and explains why we spend a ridiculous
amount of time and effort to prepare
this comprehensive guide so what we'll
cover today first we'll discuss the AI
business potential if you clicks on this
video you already realize the potential
I know so I'll just quickly share some
important stuff
and explain what it means for you if you
want to leverage this AI opportunity
then we'll dive into understanding AI
chatbot this will be the theoretical
part of the video but I will only
discuss the parts that you'll deal with
in practice okay you absolutely must
understand and be able to talk about
these aspects because in 90% of cases
you'll touch on them during sales calls
of course if your goal is to build and
then sell these shots to businesses if
you if you want to learn more about llms
promting neural networks and other
fundamentals check out the suggested
videos in the description I highly
recommend Andre kpa's 1hour talk on
intro to large language models also
check out the IBM Channel they have a
great playlist called understanding AI
models and one of my favorite channels
is three blue one brown which also has a
fantastic playlist on neural networks I
also included a chapter on prompt
engineering because it is a skill it is
a skill that you need to learn in order
to build efficient AI chatbots I'm going
to share some tips and hacks on
prompting that can save you a lot of
money so make sure to use them next I'll
share some interesting use cases all of
them are from real life experiences as
we get daily leads looking to implement
AI Solutions in their businesses we have
plenty of stories to share and then in
the Practical part of the video I'll
review the most useful tools for
building and deploying AI chbs I'll show
you how these tools have evolved over
the past year adding some very
interesting features finally we get to
the most exciting part the Practical
tutorials you might be tempted to skip
everything and just jump right to this
but I urge you to give the theory part a
chance because you need to understand it
before implementing these solutions for
the tutorials I'll guide you through
comprehensive Solutions step by step
sharing my screen first I'll use a no
code chatbot Builder to create a basic
customer service chatbot then I'll show
you how to build a more advanced chatbot
introducing a product recommendation
algorithm after that I'll demonstrate
how to use custom code and different
llms such as Cloe and Gemini explaining
which cases each llm is best suited for
and eventually I'll show you a
comprehensive AI chatboard capable of
providing customer support recognizing
images and recommending real products
based on the customers needs so we've
packed a few tutorials into one
comprehensive video to make it as
valuable for you as
possible I could spend a lot of time
discussing AI Trends and growth rates
and by the way the AI Market is
estimated to hit $1 trillion in the next
six years numerous studies show that the
adoption rate of AI am businesses will
keep increasing as shown in this slide
but I will only say one word that should
matter most timing obviously this is a
technology Revolution every company is
diving into it we just had Apple's WWDC
event introducing Apple intelligence
which means hundreds of millions of
people will start using AI accelerating
Mass adoption even faster it's clear
that we are quickly heading into a world
where you can just take your phone talk
to it and it responds intelligently
knowing you and historically the
penetration of new technology ology
typically begins with businesses to
Consumer b2c Market before expanding
into the B2B sector businesses to
business it happened with social media
platforms it happened with the internet
and it's just beginning to happen with
AI in the last 20 years we've seen
businesses go digital companies made
websites set up social media profiles
and moved from offline to online ads the
next big thing is going to be automation
especially with AI getting into to
business processes this change aims to
cut costs save time and overall boost
efficiency and if we look back at the
early days of going digital companies
that focus on making websites and doing
social media marketing made a lot of
money because the timing was right now
we have a similar opportunity with AI
agencies that focus on AI and automation
will be key in helping businesses get on
board with these new technologies as
more businesses start using AI you want
to get in as early as possible this
chart shows the technology adoption
cycle and we are still at the early
adoption stage which represents only
13.5% of the market when the early
majority and then the late majority
start looking to implement AI Solutions
you want to already be established as an
expert with track record with case
studies in your portfolio again right
now only a small number of businesses
use AI but that's changing fast so the
best time to start building and selling
these Solutions particularly AI chatbots
is
now all right let's talk about chatbot
just to make sure we're on the same page
we've got two types here old school
rule-based and the new AI powered ones
the old school rule-based chwat are
pretty limited and manual they work by
following a set of predefined rules
which means they can't handle anything
outside those rules on the other hand AI
powered chatbots use large language
model to understand the user's query and
provide an answer that's obvious right
basically chat GPT is also a chatboard
but for our Solutions we connect the
same GPT model to a different interface
with a different context now I'm going
to simplify a lot of things but I'll try
to structure how it all works so that
you understand the main logic I like to
think of it as three main elements user
prompt knowledge based and llm so it
works like this the user asks a question
or makes a request then the chat
searches its knowledge base to
understand the prompt next the AI
processes The Prompt and uses the
knowledge base to create an answer and
finally the chatboard provides the
answer to the user but the issue is the
token limitations every llm has a token
limit you probably heard of it for
example GPD 3.5 had a limit of 4,096
tokens the latest and most advanced GPT
40 has a
128,000 token limit so compared to a
year ago you've got much more
flexibility when it comes to token usage
but keep in mind tokens get used up for
three things the first one is processing
the user's query the longer you prompt
the more tokens will be used second
pulling information from your knowledge
base tokens are used both for quering
the knowledge base and for the
information it retrieves and third
generating a response so the length and
complexity of the response consume
tokens too including interpreting your
input and adding relevant info from your
knowledge base and even though GPT 40 is
twice as cheap as the previous gp4 turbo
it's still 10 times more expensive than
GPT 3.5 turbo so if you can achieve your
goals using a cheaper model it's always
better to go for it to get around the
token limits we use chunking it means
that your knowledge base is split into
chunks of text and the AI picks only the
relevant chunks to answer the the users
prompt that's why our still high level
but slightly more detailed framework for
the chatbot looks like this the first
step the user enters a prompt the Second
Step break the knowledge base into
smaller chunks the next step is to
retrieve the most relevant chunks based
on the users's prompt then create a new
prompt that includes the users's
question and the relevant context from
our knowledge base then feed the new
promp to the language model and finally
return the generated answer to the user
let's visualize it here so we have
knowledge base plus user prompt then the
system creates a context aware prompt So
based on the relevant chunks of our
database to the user prompt and then llm
generates the final result but the
problem is with step number two how do
we decide which chunk of text is
relevant to the user's query a common
solution is to use embeddings embeddings
capture the semantical aspects of texts
let's use this graph to explain each day
of the week is represented as a point in
space okay the positioning of these
points shows how closely related their
meanings are for instance the days
Monday Tuesday Wednesday Thursday and
Friday are grouped closely together
indicating that they are semantically
similar they are all weekdays right
similarly Saturday and Sunday are also
close to each other representing the
weekend and they are all close together
because they are all days of the week
the embeddings work by converting words
phrases or other pieces of text into
numerical vectors as you can see on the
right hand side here these vectors
capture the semantic meaning of the text
and when displayed in a
multi-dimensional space like in this
slide similar meanings are placed near
each other so let's summarize just to
make sure we are in the the same page
words or phrases that have similar
meanings or context will be positioned
close to each other the further apart
two points are the less similar their
meanings are and all the words or
phrases are given an embedding Vector
the numerical one right and by comparing
the distance between two embedding
vectors you can measure how similar
their meanings are I hope that's clear
it is important to understand this if
you actually want to solell these
Solutions in real life okay now we have
an updated highlevel framework for our
chatbot when the user enters a prompt
the the system starts by chunking right
which means it divides large texts like
it breaks your large knowledge base into
smaller manageable pieces then it
converts the data into numerical vectors
or embeddings that capture their
meanings and this allows the system to
compare and retrieve similar information
effectively next the system creates an
embedding for the user prompt and
searches the embedding database for the
chunks of information that are closest
to this prompt embedding it retrieves
the actual text of the most relevant
chunks and creates a new prompt that
combines the users's question with
context from the database this revised
prompt is then sent to the language
model which generates the answer so the
key is not that the AI knows everything
but that it's smartly retrieves and uses
the most relevant information it's like
a librarian fetching the right books for
you rather than knowing everything off
the top of their head all right guys if
you managed to understand this you can
be proud of yourself there are a lot of
people talking about AI on YouTube who
don't even get this high level framework
moving on let's discuss
prompting I promise to only touch on the
theory you need to actually build these
chatbots in practice and effective
prompting is one of those essential
things because it directly impacts the
cost and the efficiency of your chat
Bots I used to think it was
straightforward there are so many videos
with perfect formulas for promps on
YouTube and you know if your goal is to
just use CH GPT to revise your emails
that might be enough but if you want to
build AI assistants put them to work or
even sell them to clients you need to
understand that there is a science
behind it and if you don't learn it
you'll struggle to get the proper cost
efficiency ratio to be able to sell to
anyone so I want to break it down for
you and while doing that we will write a
good prompt which we will use later in
this video when I build a chatbot live
the chatbot will serve as an online
Beauty Store consultant for an imaginary
brand bosar cosmetics it will be able to
provide customer support recognize
images and recommend relevant products
to users what I'm sharing with you now
is based on research papers not just my
own experience scientists have tested
various prompting techniques and
measured their impact on efficiency in
the video description I'll provide links
to all these research papers so you can
check them out yourself there are two
types of prompt engineering
conversational and single shot
conversational prompting is suitable for
small tasks or personal use you know
when when you ask CH GPT to fine tune
your email and if it doesn't do well on
the first try you can follow up until
you get the output you want single shot
prompting however is important for
automating systems and creating scalable
AI Solutions this is what we aim to do
here this method involves crafting a
prompt that provides all the necessary
information in one go which is essential
for large scale and kind of more complex
applications there are no follow-ups
okay so the main components of a good
prompt are role task specifics context
examples and noes each of these
components is supported by a prompt
technique that has been researched and
backed by scientific papers these
techniques are rooll prompting Chain of
Thought prompting emotion prompt F short
prompting and loss in the middle effect
let's quickly cover each of these
components and the relevant prompt
technique and then we'll move on to the
next chapter the first component is roll
and the relevant prompt technique is
roll prompting roll prompting is a
technique where the language model is
assigned a specific role to play during
the interaction for example you are a
high highly qualified and experienced
online Beauty Store consultant you are
the best at selecting the perfect Beauty
and makeup products to meet each
customer's unique needs super simple I
know you are already familiar with this
technique but make sure to use it
because research shows it can increase
output accuracy by up to
25% this is especially true if you not
only describe the role but also provide
a complimentary description of their
abilities complimentary description so
in my case the role is you are a highly
qualified and experienced online Beauty
Store consultant and the complimentary
description is you are the best at
selecting the perfect Beauty and makeup
products to meet each customer's unique
needs the next component is Task and the
correlating prompt technique is Chain of
Thought prompting that's where we tell
it what to do Provide support generate
text Etc it should be concise and
specific okay Chain of Thought prompting
involves instructing the model to think
step by step essentially giving it a
detailed process to follow research
shows this technique can boost output
accuracy by up to
90% for complex tasks which is a
significant boost right here's a
screenshot from one of the research
papers I mentioned it shows an example
of this prompting technique where Chain
of Thought reasoning is highlighted so
we can see the difference in handling an
arithmetic task but let's have a look at
our example okay so I always start with
a verb provide customer service and
advice on services available at bossar
cosmetics and then I provide a
step-by-step process instruction follow
this step-by-step process to ensure your
script is first class first greet the
customer warmly and answer any questions
they might have step two identify
customers needs ask what kind of beauty
products they are looking for skin care
makeup hair care or something else step
three gather detailed information ask
them about their skin type specific
concerns and the look they are aiming
for step four request an image of their
face for better assessment because we
are going to recognize images Step five
suggest products based on the customer's
needs and available products in the
store step six explain how the
recommended Products address their
specific concerns or solve their pain
points step seven let them know they can
reach out for further assistance after
their purchase next we have specifics
and the associated prompt technique is
emotion prompt this section is where you
can list the most important notes about
executing the task outlined above in our
case we can add specifics such as check
the product database before recommending
products to ensure they are in stock or
if you can't find the right products to
satisfy the customer needs encourage
them to search the site themselves the
emotion prom technique involves adding
short phrases or sentences with
emotional stimuli to the original prompt
this method has been shown to boost the
accuracy of generated output by up to
115% for complex tasks so you can get
better results by adding more bullet
points with phrases like your role is
vital for the whole company both I and
our customers greatly value your
assistance and recommendations I know it
sounds strange and you might think it's
nonsense but I encourage you to check
out the research papers to see how was
measured and studied for the next
section context we're going to combine
both rooll prompting technique and
emotion prompt this section's goal is to
give context about the environment our
llm is working in and why it's doing its
specific task we want to explain its
role within our business context and
kind of hype it up adding some
additional stimul to show how important
the chatbot role is so we want to use
phrases like you are the world class
assistant and your expertise is highly
important to the company or you are the
most important component of our business
processes people that you are advised to
rely on you as never before something
like that here's my example our company
sells highquality Cosmetics like skin
care makeup Hair Care and More We value
our customers and our goal is to solve
their pain points that part provides
context about my business then Your Role
is to provide customer service
understand customer needs and recommend
products that meet those needs here I
describe its role within our business by
accurately identifying customers needs
you directly contribute to their
well-being and the growth and success of
our company therefore we greatly value
your attention to customer service and
need identification and here I add some
emotional stimuli to show how important
its role is so basically there are two
things things to remember about context
explain the chatbots role in the
business context including details about
customers types of services or Products
Company values Etc then emphasize its
importance with emotional appeal okay so
you want to highlight its impact on the
business and The Wider Community or even
the whole society if that makes sense
next section is examples and the
associated technique is few shot
prompting which essentially means that
we provide several examples while zero
shot prompting means there are no
examples given and one shot means there
is one example provided so according to
studies the accuracy can be increased by
up to 57% if you provide multiple
examples so on the graph here you can
see that the accuracy can be increased
by 40 something per if you go from zero
examples to at least one and then if you
add more you can get even higher
accuracy one thing you should remember
though is that token usage involves
processing your prod prompt we discussed
it already right so the more taex you
include in your prompt the higher the
token usage and you pay for those tokens
so keep the prompt as brief as possible
while implementing all these techniques
to achieve the best result you could add
thousands of examples but then your
prompt will be huge and it will be more
expensive to process it so in practice
we use four to five examples on average
depending on the context and usually it
is enough to achieve the best
performance and also it is important to
provide examples that the system
struggles with we usually start by
testing it during testing we identify
the types of queries that are most
difficult for the model to answer then
we take those queries and provide the
ideal outputs as examples in the prompt
just a little life hack for you and this
is also an opportunity for us to teach
it how to structure the output by
providing specific examples for our
beauty store we could provide examples
such as these so it could be typical
question by the customer and then the
ideal of output the ideal answer by the
chatbot so hi I have really dry skin
especially during the winter months can
you recommend some products to help with
hydration and then the ideal answer by
the chatbot I won't waste your time
reading out loud the the rest of the
examples you can pause and just check
them out let's move on to the last
section which is for nodes this is your
final opportunity to remind the model of
key points and add any final guidelines
to get the output right also it's a good
place for some cool hacks I like to
include things like letting the model
say I don't know this is a great way to
prevent hallucinations you allow it to
say I don't know instead of making
things up so definitely use it then
giving it room to think this allows the
model to draft better responses because
you kind of allow it to take time and
think about it other words you allow it
to use this step-by-step thinking
process and once again be encouraging
remember you are the world class expert
in X that helps a lot one thing to keep
in mind is the loss in the middle effect
studies show that language models do
best when important information is at
the start or end of The Prompt if your
prompt is long stuff in the middle might
get overlooked as you can see on this
graph which is again a screenshot from a
research paper I didn't make it up so
keep the notes section short and focus
on the most important functions and the
style you want okay for our example I
could come up with notes like this if
you don't have the answer to a query you
can say I don't have an answer please
send your query at support bossar
cosmetics.com then before answering the
query take a deep breath and think
through it step by step okay then you
are the world class expert in beauty
industry and something like your tone
should be friendly and your main goal is
provide the best customer service
service all right this is our final
example prompt broken into sections
which we will use later when building
chatbots and finally a few more tips
regarding prompting number one Implement
all of the techniques we've talked about
if you do that you can boost your
performance by up to 300% number two
prompt length and cost so for high
volume tasks keep your prompt short and
to the point because obviously each time
it runs you are charged for the input
tokens so a shorter prompt means lower
costs and we always want to keep the
system as cheap as possible as long as
it can complete the task okay number
three be smart about choice of model
good prompt engineering can make cheaper
models work better here's a strategy on
how we sometimes approach this let's say
we use openis models start by testing
GPT 3.5 turbo then test GPT 40 and if
you notice any difference if it does the
job better then you can use the results
from GPT 40 as examples within the
prompt for GPT 3.5 turbo that way you
can achieve the same results as if you
used GPT 40 using GPT 3.5 turbo for your
specific examples and you would save a
ton of money because GPT 3.5 turbo is 10
times cheaper than gp40 and then we have
temperature be a chatboard builder or an
open AI Dev platform while creating an
assistant you'll often find this
temperature in the model configuration
temperature controls Randomness so as
the temperature approaches zero the
model will become deterministic and
repetitive and that is what we usually
want because we aim to achieve
consistent and predictable results with
our AI system in most cases so we
usually set the temperature to zero one
of the exceptions might be if you want
to do some kind of creative wri or
ideation or something like that okay so
then you can test higher levels for the
temperature but usually by default we
set it to
zero now that you understand how AI
chatbots work and how to craft and test
the best prompts let's quickly review
the use cases there are obvious benefits
like improved customer engagement like
24/7 availability right they offer
global Service by communicating in
multiple languages and of course you can
save on costs by reducing the need for a
large customer service team these shads
can increase Revenue through
personalized Communications and upsells
and at the same time provide data
analytics to you these benefits are
already significant and they come from
basic AI chart bards but on top of that
you can build a lot more automation for
example lead qualification and
customized sales funnels are among the
most popular requests we get at our
agent gen to convert leads effectively
businesses need to Target them with
tailored sales funnel right to address
their specific pain points a general
sales funnel for All Leads results in
lower conversions for instance at our
agency we build chatbots for customer
support we build voice Bots to handle
calls and serve as receptionists and we
also automate social media management
different leads are interested in
different solutions an AI chatboard can
qualify leads identify their specific
needs and run them through customized
sales funnels offering targeted
Solutions instead of a one- siiz fits
all approach and that is how you can
help businesses dramatically increase
their conversion rate another great
example from real life projects is
product recommendation combined with
website scraping for Affiliates an AI
chatbot can provide customer support
match clients with the best products and
then scrape websites like like Amazon to
recommend the products while attaching
affiliate links you know on Amazon you
can get an affiliate link and earn some
commission fee on each sale so that is
what Affiliates start using these
chatbots for matching users with
products and fetching products with
affiliate links already in real time
basically selling them to leads right
away and there are many smart and
creative ways to utilize AI chatbots
obviously the more experienced you are
the more advanced you are in software
development the more comprehensive
projects you can take on and if you are
interested in exploring more use cases
check out my video titled top five AI
automations to sell in 2024 where I
cover more use cases and
projects moving on let's review the
entire toolkit you need to get started
in this space my toolkit here is kind of
default one the same tools Liam showed
in his video 10 months ago I'll quickly
go through them and show you how they
changed and the new features they offer
because now you can achieve much more
using the same tools okay so here's our
metrix prototyping software they are
extremely easy to use right you can
create a basic AI chatbot in a few
clicks but they are still quite Limited
in terms of customization okay chatbase
a year ago in chatbase you could add
documents or paste a website URL to use
them as a knowledge base for an ahr and
you could only deploy it to a website as
a widget you know now in addition to
documents and website data you can use
notion so all your pages in notion can
be used as a knowledge base they've also
introduced a bunch of Integrations so
you can deploy the chatboard to Whatsapp
which is very useful today and you can
also integrate it with zapier slack or
WordPress let's now create a customer
service AI chatbot and deploy it to a
website using chatbase just to show you
how quickly you can do it this is going
to be our demo web page it's for bossar
cosmetics and we want to deploy a
chatbot here we can see there are no
chatbot widgets displayed at the moment
so let's go to chatbase and click create
new chat button right away you are
prompted to add your data sources you
can add files text websites or connect
notion this is something new right so
let's try this out I'm going to click
connect notion I understand here we
select which pages to use I have my
bossar Cosmetics knowledge base prepared
so I'm going to allow access to that one
by the way all of these sample resources
such as knowledge base prompt site HTML
and the entire presentation will be
available for free in my school
community and you can access it using
the link in the video description let's
click create chatbot it takes a few
seconds to create all right so basically
it is done you can check which model you
are using um such as GPT 40 in this case
in the activity section you have chat
logs and analytics if you go to sources
you can add more knowledge based files
at any moment the connect tab here you
can embed the chatbot to your site share
it in a separate URL or integrate it
into WhatsApp or any other apps that we
discussed a moment ago in settings you
can go to Ai and select a large language
model you can modify the r instructions
we have some preconfigured default roll
and constraints here here but you
remember that promt that we kind of
created together according to the best
prompt techniques let's just copy and
paste it here and temperature unless you
want to use it for Creative tasks like
creative writing or ideation just keep
it at zero now you can customize how it
looks change the colors the icon Etc and
you can embed or share it just make it
public and let's quickly test it out on
a separate page first okay hi what are
you your products hello welcome to
bossar Cosmetics we offer a wide range
of high quality Beauty and skincare
products and then it provides me with a
list of products nice it works well now
to actually put it on our website I'm
going to copy this script here then go
to the HTML of the website and paste it
somewhere somewhere here save it then go
back to the website refresh it and we
have our chatboard widget displayed in
the corner hi do you have any hair
products yes we do have a variety of
hair products available are you looking
for shampoos conditioners let's say
shampoos which ones do you have and it
gives me options available according to
my knowledge base in notion and that is
how this simple prototyping works you
you can set it up in a few minutes you
just need to have your knowledge base
and prompt or roll instructions prepared
okay danta AI dant AI is also a great
tool for prototyping a year ago you
could use documents and websites as a
knowledge base and you could add a
YouTube url which would be automatically
transcribed and used as a knowledge
based now they have introduced Google
Drive and Google Sheets as sources of
knowledge for a chatbot and that's
already a lot additionally they have
preconfigured some popular
functionalities the chatbot can now
collect user data with the lead
generation forms and book meetings using
your calendar links this is how it looks
like you can click create AI chart Bots
let's call it bosar Cosmetics assistant
click next and you can either upload
files or URLs it could be YouTube Google
Drive Google Sheets or website I have my
product database in Google spreadsheet
with product names and pricing so I'm
going to copy this link and paste it
here click next review and conf firm
then create the chatbot okay now it
should use my data for example we have
this product name and the price for it
is
$4.99 let's ask what is the price for
and paste that product name it replies
the price is $14.99 so it is
successfully using my Google
spreadsheets as a knowledge base and
that's great on top of that I love the
user experience here the system guides
you through the customization steps you
can modify the appearance of the chatbot
you can add your logo the chatbot URL
the next step is the chatbots
personality so there are some
preconfigured templates for you to
choose from or you can create your own
prompt so I'm going to use my prompt
again just copy and paste it here and at
the bottom you'll see chatbot creativity
which refers to the temperature right so
they just named it differently but it is
the same thing it determines how
creative or random the responses might
be next you can change the welcome
message you can add some suggested
prompts and if you choose so always they
will be displayed here on the right
let's say what are your products and it
looks like this next is lead generation
and this is really impressive listen I
have a video where I built a lead
generation chatbot in voice flow and it
was quite complex there were a lot of
steps involved in in that I also used
mag.com to connect the chatbot with
Google spreadsheets using web Hooks and
I had to set up the triggers and so on
but using dant AI you can achieve the
Same by just checking this box and
describing when you want the lead
generation form to show you can allow
the user to skip the form you can
uncheck the option to show it at the
start and instead describe a condition
when the form should pop up for example
when the user asks to be contact Ed by
agents so whenever they ask to speak to
a human agent the chatbot would collect
their contact details and this way you
generate leads right you can also add
more Fields like phone number name email
Etc all right and another big update is
booking meetings you can just paste your
calendly link and describe when you'd
like the book meeting button to appear
for example when the user asks for a
meeting or you can also set it to always
be visible um and it looks like this at
the bottom of the chatbot window I
really like these two options they are
of course for paid users only so you'd
have to upgrade to use them but as for
prototyping software now there is much
more flexibility they also offer some
Integrations here you can connect your
chatbot with WhatsApp messenger zapier
and more and they made it very easy I
mean they provide you with a detailed
step-by-step integration guide so if you
want to connect it to Whatsapp you don't
even need to search you know for YouTube
tutorials or something like that it is
all here then we have chatbot Builders
which are much more flexible you can
Implement more advanced features using
tools like voice flow or botpress but
they are harder to use they have kind of
a modular structure requiring you to
build the chatbots workflow logic step
by step step it's not as userfriendly
and preconfigured as chatbase or Dante
and to create really Advanced features
with voice flow or botpress you often
need to use some some web hooks or write
a few lines of code so I'd say that
these are low code rather than no code
tools when it comes to building more
advanced Solutions okay comparing the
two B press is definitely more
complicated and requires more technical
back ground so I put together this
comparison table bpress is an open-
Source conversational AI platform which
means it's flexible but might require
more Hands-On work okay voice flow on
the other hand is a no code platform so
if you're not into coding voice flow
might be easier to get started Target
users bpress is geared more towards
developers and businesses while voice
flow targets designers product managers
and also businesses so again it's more
user friendly especially if you don't
have a developer background
customization bpress offers extensive
customization with its modular
architecture you get a lot of control
here Voice Low is more limited to the
platforms features right it's
straightforward but less flexible
hosting bpress is self-hosted you have
to manage your servers Voice Low is
hosted by voice flow so they handle the
hosting which is one last thing to worry
about pricing they both have free plans
available so you can test them out right
away overall pros and cons bpress gives
you full control over data and
deployment it's highly customizable if
you have the skills but it has a steeper
learning curve at the same time voice
flow is userfriendly and still provides
enough customization to be far more
advanced than chatbase or dant however
comparing to bpress you'd be more
dependent on their preconfigured
features and have less control over
deployment so there is always this
tradeoff you know between flexibility
and ease of use I'm going to provide you
with a voice flow tutorial later in this
video so just stay tuned okay then we
have what I call integration tools and
there are many options however mag.com
and zapier have proven to work well in
our context I mean with these tools you
can build workflow automations that in
enhance the capabilities of your AI
chatbots for example if you need to
establish communication between your
chatbot in voice flow and a third party
tool like Google spreadsheets or a CRM
system or many other apps you can use
m.com to create a scenario and just drag
and drop these apps to connect them
instead of coding the API integration if
we compare them I'd say they are quite
similar in terms of what you can achieve
just as bpress is technically Ally more
advanced compared to voice flow in this
case mag.com can be more complex for
non-technical users sometimes building
Advanced scenarios requires some
technical background to kind of set it
up properly other than that they are
both drag and drop Solutions used to
build workflow automations and both
offer very generous free plans so if you
want to do something like complex
workflows requiring multiple app
Integrations such as connecting chatbots
to CRM systems or creating customer
support tickets from chatbot chats I'd
go with me.com for its flexibility and
ability to handle more sophisticated
scenarios but if it's simpler or more
straightforward automations you are
after like automatically sharing new
blog posts on social media or
automatically creating tasks in project
management tools for example in jera you
know triggered by new emails or form
submissions for that I'd go for zapier
for its userfriendly interface and
really extensive library of predefined
templates and since these tools are
probably the most popular you can find a
tutorial on YouTube for each of the
automation tasks I just mentioned and
that way you can learn them you can
learn how to use them in no time really
and then we have custom code option this
is what we do as our agency and just to
give you more insights we use nodejs for
all our functions sure you can get the
same results with other programming
languages and we know python we know PHP
and rust but we mainly stick to
typescript and nodejs because we have
the most experience with them okay we
use AWS S3 to store files we deploy our
functions to AWS Lambda of course the B
response time is important so you need
to reduce it as much as possible we use
LRT as the run time in our Lambda
functions to achieve this don't
overthink it okay I just mentioned this
in case you have a technical background
and are curious what our devs use so why
did we choose to custom code our
Solutions instead of building them in
voice flow and connecting to other apps
using mag.com for example first of all
this is basically the cheapest approach
because you pay fewer third party margin
Fe second it is the most flexible
solution because are not dependent on
what was preconfigured by the voice flow
development team you can create any
solution according to the client's needs
I'll give you an example let's say we
want to build an AI powered customer
support chatbot that can recommend
products based on customer needs you can
build a chatbot in voice flow Implement
a product recommendation algorithm then
connect it to make.com store the product
database in air table or Google
spreadsheets use web hooks to connect
your voice flow chatbot with mag.com and
by the way I'll show you how to do
exactly that in a moment or you can
write custom code build an AI assistant
and connect it via API to air table or
Google spreadsheets or whatever it is
you want you'll achieve the same result
but the second option is more flexible
for example if you wanted to add image
recognition on top of that using B press
or voice flow wouldn't be possible
because they don't support image
recognition but but using custom code
you could just modify the code and add
new features of course the entry bar
here is higher because you need a
software development skill set I must
say though that out of all the leads
we've had about 80% of the projects
would not be possible to complete if we
only used no code or low code
Solutions now let's move on to the
Practical tutorials we already built a
basic customer support chatbot in chat
base this time I'm going to make it a
bit more advanced I'll show you how to
build a customer support chatbot which
will also be capable of recommending
product listings to customers and for
that I'm going to use voice flow as a
chatboard builder then Google
spreadsheets to store my product
database and mag.com to connect voice
flow with Google spreadsheets so that my
chatboard could have access to my
inventory in real time all right let's
start with the demo here's our demo
website I've already added our chatbot
widget let's start a new chat how can I
help you find the perfect product okay
can you recommend a serum and it
provides me with product listings these
are the serums available in my product
database there are buttons to visit the
product page and to purchase each
product also has a brief description
next let's ask can you also recommend a
scrub anything under $8 and yep it
recommends the treeh hot she sugar scrub
it's a great option within your budget
and it's recommended only one product if
we go to our Google Sheets where I store
my products and check the subcategory
column we'll find scrubs there okay
there are two scrubs one below $8 and
one above that's why it recommended only
this one which satisfies my request this
is how the whole chatbot looks in voice
flow it's not too complicated and we are
going to build it together from scratch
we will use this spreadsheet as our
product database I'll also upload this
knowledge base which is for my fake
online store you remember bossar
Cosmetics once you sign up with Voice
flow click new agent enter the agent
name let's say bosar Cosmetics assistant
select modality chart and select English
and create the agent this is going to be
our workspace okay let's delete all
these beginner tips here and first we
want to add a knowledge base go to
knowledge and click add data source here
I'll add it as a plain text select all
of it and copy paste it here now it has
some information about bossar Cosmetics
let's go back to workflows click edit
work close and here we will start
building I made it extremely easy for
you every step is described in a word
file that I will also attach in my
resource Hub in school Community it
details every step and when I say every
step I mean if if it says talk text it
means you go to talk and then text so
there should be no confusion at all also
you have all the text and code that you
can just copy and paste such as this
welcome message I'm going to generate a
few more variants and this is how
detailed this guide is so feel free to
use it okay so go to listen buttons then
click no match and create a path
connected to a new blog it should be
logic set name it set AI question and
apply to Let's create a new variable
name it question and as a value select
last utterance which is the reply from
from the the user in the chat okay
connected to the next block AI set AI
select AI model as a data source and
paste this prompt classify whether this
user is asking for a product
recommendation or not if they are asking
for a product recommendation say yes if
not say no apply this to a variable
recom and create the variable next add a
logic block choose condition set it so
that if the variable recom contains yes
it will go to the product recommendation
algorithm if no other words no match
create a path and it will go to the AI
text block so the next block is AI set
AI here keep AI model as a data source
again and paste this prompt here is what
the customer has requested we have our
question reply to this question
according to the knowledge base if you
don't have an answer refer to support at
postar cosmetics.com and then create a
new variable let's call it recom text
and apply it let's label it AI text and
now go to talk text drag and drop it
here and select our variable Recon text
then connect it back to the first block
okay so this part is done it can already
provide customer support and determine
if the user is asking for product
recommendation or not let's mark it with
one color okay the idea here is to check
if a product question is asked if yes it
will make request to me.com if not it
will return us back to the first blog
okay let's run a test welcome to bossar
Cosmetics when are you open the response
is thank you for reaching out to bossar
Cosmetics our store hours are Monday to
Friday blah blah blah so according to
our knowledge B it replies correctly now
we need to build the product
recommendation part let's add a new
block logic set here we need to set our
Google Sheets variables so this part of
the assistant will be responsible for
running air table request sorry I meant
m.com request not air table here we need
to set Google spreadsheets IDs let's add
a few sets the first one is applied to
spreadsheet ID let's create this
variable okay then go to your Google
spreadsheets URL and the spreadsheets ID
is this part after d slash up until
sledit paste this ID as a value here
with a quotation marks the second one is
the uh sheet ID create the variable and
you can find this ID in your Google
spreadsheets URL again after GID equals
so in our case it's zero next go to log
iic set this will be our main Google
spreadsheets logic okay it will set the
number of Google Sheets row responses
apply to number of responses okay create
variable and I want to set it to four to
have only up to four product listings in
the chatbots reply okay then go to AI
set AI drag and drop it
here this will be our Google
spreadsheets query choose AI model as a
data source and paste my prompt which is
convert the following query to Google
charts query language if there is no
valid query reply with there is no valid
query the query should only at Max
include the product category create a
variable spreadsheets query and then go
to the prompt settings and paste my
system prompt from the guide obviously
you'd have to modify it according to
your needs according to your product
database and your context but overall
these instruction describe how to
convert user queries into queries for
Google Sheets we provide the column
names according to the columns in Google
spreadsheets it should be a b c d we
list the products product categories and
subcategories and provide a few more
instructions here for example if they
ask for something that is not listed
just assign a product that is close to
what they want for example they ask for
a shower gel just assign body wash
subcategory since it is close to
accomplishing the body washing purpose
of the product you are only to answer
with the query for example input do you
sell any serums assistant like the
output should be only the subcategory
all right the next text block is just a
logic block so go to logic condition if
query that would be a new variable you
need to create it if it contains no
valid query go to the AI text block the
one we created here so it will go back
to the loop okay and if no match if the
query is valid then we want to make a
request to make thatc so let's add
another block which would be DAV API and
in this block we'll configure the API
call to mag.com we need to set what we
are going to pass to mag.com so first we
need to switch this to post now I want
to add the body we are going to send the
query which will be the spreadsheet
query it was set in the second block
here then we want to add the spreadsheet
ID the one we set in the first block and
also the sheet ID which was also set in
the first block then we have capture
response let's set the response and
apply it to formatted response variable
and we need to create this new variable
okay if it fails we need to add a new
text block and say something like sorry
something went wrong please try again
and I'll generate more variations here
let's also Mark this block as failed and
give it a red color if it succeeds we
will continue okay but for now let's set
up a web hook for mag.com go to mag.com
sign up and create a new scenario here
the first component should be custom web
hook create a new web hook let's name It
bossar Cosmetics voice flow and save
okay it will be waiting for variables
select this URL copy it and paste it
into Post in our API blog in voice flow
now let's send our variables to mag.com
click run run
test say um recommend me a serum it
should go through the whole logic here
it should be successful but we haven't
built the following block yet however in
mag.com our data structure is
successfully determined the next block
should be Google Sheets so scroll down
and select search rows Advanced okay
connect your Google account here and
leave enter manually we need to select
our variables so spreadsheet ID sheet ID
and query and set the maximum number of
return rows to 10 okay by the way for
mag.com I will also attach this guide so
you can follow it step by step without
any trouble now the data we receive from
Google spreadsheets will be aggregated
into Json and then the Json string will
be returned to our voice lowboard so add
a
Json aggregate to Json The Source model
should be your Google Sheets here here
data structure I just select product cuz
I have this preconfigured data structure
in your case you'll have to configure it
so you'll have to click add and add
items according to your column names
such as product name then add item again
category you want to add all these items
one by one now you see I don't have any
values populated from my spreadsheet yet
so let's run the whole thing again to
populate some values click run once run
anyway run the whole thing again okay
recommend me a serum so it's going to
query the Google Sheets now and if I
switch back to mecom and go to that Json
component I should have these values for
my columns populated product name
category subcategory description price
and image link the last block is web
hook response it's going to send the
Json string back to our voice flow bot
okay for body select Json string and I
also want to add some custom headers
here so content
type and application Json click okay and
make.com scenario is now set up let's
reset test run it and say recommend me a
serum it's going through the flow and in
me.com everything is initialized and
finalized successfully just make sure
that if you go to scenarios this
scenario is turned on okay to make it
work the next block is dev then
JavaScript this block takes the Google
spreadsheet data and converts it into
variables basically this part of the
system is to run our make.com request so
let's mark it with one color for the
JavaScript block you need to enter the
JavaScript code here just copy and paste
it from my guide so we get the response
from meg.com then product count
determines how many products it returned
if more than zero then we set the
variables here very repetitive code to
be honest but you know for this
structure you'd have to do it obviously
modifying it using your names of of your
own columns if it fails go back to the
AI text block and start over if it
succeeds we add a new block which is
logic condition and this part is just to
set the logic and make it display the
right amount of product listings
according to the amount of products we
got in the response from mag.com the
first one is zero let's create a
variable product count if the product
count is zero create One path then
another condition if product count is
one and the same we do for two three
four and then no match create a path so
if mag.com returns two products then it
will go to two product listings if four
then it will go to four and if zero then
we'll send it back to our AI text
response and kind of close the loop else
means that it is not 0 1 2 3 4 so it is
five or more and in that case we want to
display also four products because
that's the maximum amount of product
listings we want to display so I'll
connect it to the same product listing
blog as if it was four products next we
need to create four blocks it will be AI
set AI the goal of these blocks is to
create the follow-up messages to support
the product listings right to to
describe the suggested products so text
one select AI model as a data source
then just paste my prompt here here's
what the customer has requested our
variable for question here's the query
that will be ran spreadsheet query
here's the product recommended product
one name which is our variable for for
the first product if the product
recommended is not what they asked for
please tell them that we don't have what
they are looking for but we found this
as a close alternative and we want to
apply it to our variable recom text to
be more specific you can provide a
system prompt here and I like to do that
usually something like your job is to
help the customer understand the
products they were recommended your
answers need to be short and concise Max
one to two sentences above this message
will be the product listed so we don't
need to ask if they want to see them and
don't ask any queries just to be safe
all right duplicate it three times and
add more recommended products here is
the first product recommended the second
the third and the fourth and then just
modify the second and third blocks here
accordingly the last step for the whole
system is to display the product
listings add a new block talk then
Carell here you want to switch to link
and create a few variables the first one
is product image created the second one
should be product name okay and the
third one product price then we can add
some buttons here for example visit
product and if you have a website with a
product listing you should go to actions
select open URL and paste your url the
second button can be purchase and again
you can add your url here if you have a
website then go to talk text drag and
drop it here select our variable ROM
text that's the one the AI model
generates in the previous block here
according to our instructions right it
will complement the product listing with
a
description okay then go to listen
button drop it here name it like let's
start over this is just to complete the
loop so actions go to block search for
start and it will bring the user back to
the first block just duplicate this
adding more product cards according to
the product number every time you'd have
to create new variables like product two
image product two name and product two
price Etc and once it is all done our
chatboard is basically ready to be used
okay this is how our whole system looks
like let's click run recommend the best
serum you have and let's see how it
works it is going through this steps and
this is how the output looks like we
have the product listing two buttons
then a brief description of this serum
and a button to start over if I click
this button it will begin the flow from
the start so we don't have to build it
from scratch you can just modify it
according to your needs I will attach a
template to this chatbot in my resource
Hub so you don't have to actually build
it from scratch you can just import it
and modify according to your needs many
people ask me how to use the templat so
let me just quickly show you in voice
flow click on the icon in the top right
corner to import the template upload the
template and you'll be able to edit the
workflow okay for make.com create a new
scenario click on the three dots and
select import blueprint upload my
template in Json format and you'll get
access to my scenario this system is
quite basic it can only search by
categories and subcat atories and sort
by price but later in this video I'll
show you a b that can actually analyze
product descriptions and evaluate
customer needs and then match the
relevant products to customer needs now
pay attention this is important good
news if you really Master chatboard
Builders like voice flow and integration
tools like mag.com you can already do a
lot you can provide real value and there
are many tutorials on YouTube on how to
to use these tools in including my
channels so you'll have enough resources
to learn from but here's the bad news
since there are so many tutorials and
these tools are userfriendly requiring
no code no background in development a
lot of small and medium-sized business
owners would rather watch the same
tutorials and do it themselves instead
of paying you a few th000 for
implementation every second lead that
books a call with us always says well I
am technical enough I can use voice flow
and mag.com but when it comes to code
that's where I'm stuck so my point is
that if you weren't limited to no code
tools and low code tools and you could
build some kind of custom solution to
fit the customers's need then you would
have a great competitive Advantage now
the big question is where can you learn
to build custom code for these Ai and
automation Solutions
well you could spend a year learning
Python and then even more time figuring
out how to apply those skills to these
Solutions but a more efficient way would
be to take some kind of a coding crash
course specifically designed to give you
the Knowledge and Skills to build
exactly these kind of AI and automation
solutions that you can sell as an AI
automation agency and this is exactly
what we are going to offer we are going
to launch the AI Fellowship a community
program consisting of three pillars the
first one is AI automation coding crash
course we've noticed that if you learn
how to build and adopt 10 to 15
solutions for different businesses you
can handle about 80% of projects they
really repeat a lot and since we are
doing it we know which Solutions are in
high demand that's why we are putting
together a curated crash course focusing
on AI and automation solutions that are
currently being sold it is the 80/20
rule you need just 20% of the effort to
achieve 80% of the results and our team
of developers is preparing the modules
right now to provide you with that
crucial 20% of technical knowledge most
relevant to our field then you also need
to know how to sell these Solutions you
can save many months of your life by
learning from our trials and errors so
along with the coding course we'll
provide AI automation agency coaching
this is a complete guide on how to start
and scale your business you'll learn how
to generate leads and close them you'll
get an entire toolkit for running an
agency including email templates
contract templates how to price Your
solution and basically everything from A
to Z and of course the most valuable
part of the coaching pillar is the
lessons and tips that come only from
real life experience and what I believe
to be the most important part of the
whole AI
program is the community you can find
all the information and knowledge about
coding and sales on the internet we just
save you a ton of time and money by
providing curated modules but what is
not so easy to access is a community of
like-minded people working towards
similar goals and that's the third
pillar of our program we'll have
masterminds a closed Discord Community
will introduce a matching system so if
you are a salesperson looking for a
developer or vice versa will match you
and the support you'll get from other
participants is invaluable instead of
working alone you'll be a part of a
group where someone is just a step or
two ahead of you and has faced the same
challenges the community is truly the
the most important part of the program
I'll attach a link to the AI Fellowship
where you can sign up for the waiting
list the first 50 people on the list
will get a 50% discount on the entire
program so sign up now and I'll provide
more more details soon I'm going to show
you two projects with custom code AS
examples of what you'll be capable of
once you complete the course but before
that let's quickly review the top large
language models available
today it is important to know at least
the top ones because they each have
their pros and cons for different tasks
and you want to use the right model for
the right task overall there are three
models we usually test to pick the best
one for a project project Google's
Gemini anthropics clae and open AI GPT
I've put together this table to compare
the latest versions Gemini 1.5 Pro clae
Sonet 3.5 and GPT 40 I'm going to refer
to them as clae Gemini and GPT 40 not to
repeat every time Sonet 3.5 Gemini 1.5
Pro and so on so when it comes to the
context window clae offers 200,000
tokens Gemini 1 million making it
perfect for handling extensive data sets
and kind of long documents and GPT 40
provides
128,000 tokens which is more than enough
for many tasks but the smallest of the
three models here talking about speed
according to our test CLA is faster than
GPT 40 but slower than Gemini so GPT 40
is currently the slowest among the three
looking at costs Claud charges $3 per
million input tokens and $15 per million
output tokens Gemini 350 for the input
tokens and 1050 for the output tokens
GPT 40 is the most expensive with input
tokens costing $5 per million and output
tokens at $15 per million GPT 3.5 turbo
is 10 times cheaper by the way so we
usually test it if we can achieve the
same results um the same output
performance with GPD 3.5 turbo also
Gemini is the cheapest only if the
contact window is up to
128k tokens once you need to use more it
becomes the most expensive one with $7
for input and $21 for output tokens
overall when choosing between these
models try to consider their unique
strength and match them to your specific
needs if you deal with large data try
Gemini 1.5 Pro for detailed and precise
tasks especially in legal and you know
kind of educational Fields try using clo
Sonet 3.5 and for a flexible model that
performs well across different tasks GPD
40 is a good choice even though it costs
more but take this with a grain of salt
okay even though GPT 40 might seem like
the worst option right based on the
features and pricing alone it's not that
straightforward in fact we use GPT 40 or
GPT 3.5 turbo for 80% of our projects
the only way to find the best model for
your task is through testing for example
for one of our projects we needed large
context window theoretically Gemini's 1
million token limit should have worked
but it returned error in practice and we
just couldn't use it this is just
something from our experience for you to
keep in mind make sure to test it
properly now I'll show you a simple code
to build AI chart Bots using these
different models and to make it more
interesting these Shard Bots will be
able to also Rec recognize attached
images on top of customer service this
is our Gemini bot it is in ret I am
going to share this code as a template
in my resource Hub in school so you can
copy it and play around or feel free to
steal it I don't care in this project we
have only three files Gemini service JS
index JS and utility service JS this is
just a code overview okay so don't worry
if you don't fully understand it the
goal is just to show you the logic of
how it works when you join our AI
fellowship program we guarantee that by
the end of the course you'll be able to
code chatbots like this one on your own
so the first file is index JS it
contains our endpoint where requests are
made SL chat is our rout for requests
from basically any chat in use be it a
WhatsApp chat or web chat doesn't matter
it expects two Fields chat ID and
message then we have the validation if
all the data is passed through and once
validated it is passed to thees function
which is located in Gemini service JS
then in Gemini service JS The Bard
creates a local database stores the
message history or loads it if it
already exists and it creates a new
message and sends it to the chat then it
receives the result stores it in the
database and returns it the utility
service file has only one function that
uploads and formats an image into the
base 64 format which is expected by
Gemini's Library that's pretty much it
if you need to understand this better
here's a tip pause the video make a
screenshot of the code upload it to CH
GPT and ask for any clarifications you
need obviously to make it work you need
to add your API key from Google AI
Studio to actually connect it to Gemini
so go to secrets and set your API key
for that just go to Google AI Studio the
URL is AI studio.
google.com/ apppp API key you need to
log in and here you can create your API
key create API key in new project and
here it is just copy it and then paste
it in ret as value once done click run
I'm going to copy the dev URL from here
and and go to My Demo page we just
quickly created this page for the
purposes of this video just you know to
demonstrate how the chatbot works so I
go to settings enter the URL from repet
I need to add slash chart CU that's our
end point okay save it and let's try it
out hi it replies back now let's upload
an image for example this one and ask it
to list the objects in this photo okay
give it a second and it provides me with
the objects it can see in the photo we
can also ask additional questions such
as I like the keyboard tell me about it
and it responds as a general model would
usually respond that it's hard to say
anything specific about the keyboard
without more information however based
on the image I can make some general
observations and it provides me with
specifics such as type lay out color and
material all right now let's look at the
cloud board don't forget to set up
Secrets since we are using clo this time
you need to go to entropic so console.
antrop
docomo you can sign up with your Gmail
account and you'll get some free credits
to get started just click on get API
Keys then click create key give it a
name say test key and click create key
copy your key from here and paste it in
ret as value in secret okay this Cloud
board has a similar structure index GS
is identical to what we had with Gemini
it calls thees function which is this
time located in CLA service JS instead
of Gemini service JS utility service JS
is also the same it uploads an image
then in cloud service JS we create a
database create create a new message
then send it to an Tropic receive the
response store it in the database and
return it to the user okay let's test it
out I'll click
run copy the dev URL from
here switch to My Demo page go to
settings and paste the URL again don't
forget to add slash chat Okay click save
and now I should be able to try out hi
we get the response from the bot now I
will attach the same image as before and
ask it
to list the objects in this photo give
it a few seconds to process and and it
provides us with the output the bot
recognizes the objects well it even
recognized that the notebook in the
image had notes written on the cover
which is not readable for a human
without zooming right all right let's
ask it um tell me more about this
keyboard and once processed it replies
well it even identified that the
keyboard is most likely an apple magic
keyboard and provided many details so it
works great I think it's even better
than Gemini in this case that's why you
should always test and compare the
outputs those were clae and Gemini for
AI assistant using gp40 I have two
separate videos one for a General
assistant and another with image
recognition capabilities so be sure to
check them out as well and for the final
chatbot today I'll show you a bit more
advanced solution this chatboard again
it is going to be our online Beauty
Store consultant for bossar Cosmetics
this time instead of building it in
voiceflow and make.com I'll use custom
code and in addition to product
recommendations it will also be able to
recognize images so I'll basically
combine everything we did today Customer
Service Plus product recommendations
plus Vision capabilities let's start
with the demo right away to give you an
idea of how it works and then I'll break
down the code this ret template will
also be available in the resource Hub so
you can use it do not forget to set up
your secrets this time we use GPT 4 all
so you need to head to platform.
open.com go to API keys and create your
secret key once done click run wait for
the dev URL copy it and paste it on the
test page without this slash in the end
and let's begin the conversation with
the
bot hi I need skin care it takes a few
seconds to process the request and it
asks me to provide a bit more
information about your skin type and any
specific concerns you have this will
help help me to make more tailored
recommendations because remember in our
prompt we instructed the bot to find out
about customer needs first and then
recommend tailored products and this is
what it does okay let's say oily skin
and upload a photo of a face send it and
wait for the response so it says based
on your needs and our available products
here are some recommendations for
managing oily skin and address ing acne
first of all it correctly understood
that the customer needs products for
managing oily skin and addressing acne I
mentioned the oily skin but I never
mentioned acne it recognized that purely
from the image okay secondly it searched
our database for the available products
and then provided the user with the
relevant options in the end it also
provided a summary and a short
description for each of the suggested
products so let's ask it something else
I'll say excellent also recommend a
blush for me it is now searching our
database for blushes and provides us
with the relevant products along with
the descriptions also I need a scrub and
it suggested two scrubs there is a
button to view product but since we
don't have a website it doesn't redirect
anywhere but obviously if we had a
website it would take us to the product
purchase page that's it for the demo now
let's go over our code we tried to keep
it very simple without extra functions
for this project the product database is
stored in an Excel file to avoid
complicating the code with you know
Online requests and additional functions
in the assistant so this is just to
simplify the process if you want to test
it with your own product database you
need to delete this product XLS and
upload your own file naming it the same
way way and when we receive a request we
search this Excel file for the necessary
information okay index JS just like in
previous projects it's almost the same
the difference is that when we receive a
file we create an open AI file for that
we have the upload image function and it
is located in open AI service JS so it
downloads the image using a URL sends a
request to open AI purpose Vision this
is very important to make it work in the
assistant and then deletes the
downloaded image and Returns the file ID
which we received from open we then use
this file in the message that is added
to a thread okay here we have the same
thing as in previous projects it starts
with creating an assistant using the
create assistant function this function
is also located in open AI service JS
also the instructions here are quite
extensive you remember our prompt was
quite big right so they are stored in a
separate file instructions txt here it
reads this file in the second line of
code and also here we have the names of
our product database and knowledge base
really just check out my video on how to
integrate gp40 assistant to a website I
covered this whole structure there I
covered what assistant Json is for so
you'll have more understanding if you
watch that video If we don't have the
assistant Json file in the project it
will create and save one after it gets a
positive response from open AI initially
we create an open AI file for the
database and store it in a vector
database I hope you remember what Vector
database is I discussed it in the
chapter about understanding AI chatbots
if you need you can go and rewatch it
then we create a products file and we
will use its ID in the code interpreter
here there are different tools available
at openai for for example file search or
codee interpreter so for the knowledge
base it uses file search you remember
how it works right the vector database
the chunking all of that so it retrieves
the relevant chunks of text from the
knowledge Base According to the user's
input and then for the product database
it uses code interpreter it will search
for the relevant products in our Excel
file okay and here once the assistant is
created it is saved in assistant Json
file instructions txt file contains our
prompt okay the one we created in this
video and we just added some Specific
Instructions here at the end to ensure
the assistant Returns the products data
that it found in our Excel file using
Json format this makes it easier to
display the product listings and that's
it this is a simplified process just for
the purposes of this video if it were a
real project we'd make it more complex
and definitely more reliable but I just
wanted to give you an idea of how more
advanced and custom coded AI chart Bots
look guys if you manage to understand
how this code Works you're probably in
top 1% of viewers it would take numerous
videos to actually teach you how to
write code like this this isn't
something you can learn from a you know
a quick 15minute tutorial that's why we
invite you to our AI fellowship program
you'll get a complete course on this and
by the end of it you'll be very
comfortable building projects like this
one other than that if you watched and
understood this video till the end you
can be proud of yourself you are now
ahead of the majority of people who are
interested in AI now you have a full
understanding of what it takes to build
these Solutions which isn't as easy as
it might initially seem right my goal is
not to sell you this idea but if you are
serious and ready to commit you can make
a lot of money if you start start now
you can still be early enough to
leverage this opportunity and once you
learned how to build Ai chatbots and
other AI Solutions and workflow
automations you need to learn how to
sell them the best way if not the only
way to do this is through practice to
get more practice you need more sales
goals you need more leads right cold
goals don't work here not for me not for
other AI agency owners we actually
discussed this recently and everyone
agrees that it does doesn't work just
yet you need to generate warm leads I
have a video on how to start an AI
automation agency where I break down
step by step how to start and generate
the first leads the next video on the
channel will be the second part of that
video with more insights and specific
metrics I've gathered over a few months
so make sure to subscribe and not miss
it long story short at this stage the
best way to get warm leads is through
generating content putting out value
helping people and showing your
expertise at the same time that is what
I'm doing today and I hope you'll
consider it as well thank you very much
for watching and I'll see you soon bye