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

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