Generative AI Powered Assistant At Work | Amazon Q Service | AI | Generative AI | AWS

Summary

This video tutorial introduces Amazon Q, a generative AI assistant designed to boost enterprise productivity. It explains how Amazon Q can be customized with enterprise data, enabling it to answer queries and solve problems specific to an organization's IT professionals. The host demonstrates creating an Amazon Q application, training it with data, and deploying it as a web application, showcasing its potential to serve as a single, intelligent source of information for enterprise workers.

Takeaways

• 🚀 Amazon Q is a generative AI assistant designed to increase productivity in enterprise environments by providing tailored information and solutions.
• 📈 It was recently released at the AWS re:Invent conference, marking it as a new and innovative tool in the AI space.
• 🔧 Customization is a key feature of Amazon Q, allowing it to be trained with enterprise-specific data to provide knowledgeable responses.
• 🤖 The service operates similarly to chatbots like Chat GPT, but it's trained with enterprise data to answer questions relevant to the company's operations.
• 🛠️ Amazon Q can be particularly useful for IT professionals, such as cloud engineers, by providing them with workflow guidance and solutions to technical issues.
• 🔑 Benefits include engaging in conversation to solve problems, generating content, and providing answers based on the company's information, code, and systems.
• 📝 Amazon Q can be personalized based on the user's role and permissions, ensuring that sensitive information is only shared with relevant roles.
• 🔒 Security is built-in by default, aligning with AWS's commitment to privacy and secure data handling.
• 🔗 The service can integrate with various data sources, including S3 buckets, cloud applications, and on-premises databases, to form a comprehensive knowledge base.
• 💻 Amazon Q applications need to be deployed as web applications, which can be accessed through SSO and IDP for enterprise users.
• 🔍 The script demonstrates the process of creating an Amazon Q application, connecting it to a data source, and the potential for it to answer queries once trained with relevant data.

Q & A

What is Amazon Q service?

Amazon Q is a generative AI assistant service designed to increase productivity in enterprises by providing customized solutions based on enterprise data.

How does Amazon Q enhance productivity in an enterprise?

Amazon Q enhances productivity by enabling professionals to quickly access information and solve problems, mimicking the knowledge-sharing process within the enterprise.

What is the significance of training Amazon Q with enterprise data?

Training Amazon Q with enterprise data allows the AI to understand and solve problems specific to the company, providing tailored responses and solutions to its workers.

Can Amazon Q be used by IT professionals to solve technical issues?

Yes, IT professionals can use Amazon Q to get answers to technical questions, such as VM patching workflows, by querying the AI which has been trained with relevant data.

What is the role of a knowledge base in Amazon Q?

A knowledge base serves as the source of data that trains Amazon Q, providing it with the information it needs to generate intelligent responses to user queries.

How does Amazon Q ensure personalized interactions based on roles and permissions?

Amazon Q can be configured to deliver information relevant to specific roles within an enterprise, ensuring that users receive data appropriate to their permissions and responsibilities.

What is the importance of data quality for Amazon Q's performance?

High-quality, clean data is crucial for Amazon Q's performance as it directly impacts the AI's ability to understand and provide accurate, relevant responses.

Can Amazon Q be integrated with various data sources like S3 buckets?

Yes, Amazon Q can integrate with various data sources, including S3 buckets, to access and utilize enterprise data for training and providing information.

What is the process of deploying Amazon Q as a web application?

To deploy Amazon Q as a web application, one must configure an Identity Provider (IDP), upload metadata, and define attributes, then deploy the application to make it accessible via a web interface.

How can Amazon Q be tested or previewed before full deployment?

Amazon Q can be tested or previewed using a feature called 'Preview Web Experience,' which allows users to interact with the AI through a simulated web interface.

What are some of the challenges or limitations when using Amazon Q?

Some challenges include the need for a clean and comprehensive knowledge base, the requirement for an IDP for web deployment, and the initial setup and training process to ensure the AI's effectiveness.

Outlines

00:00

🚀 Introduction to Amazon Q for Enterprise Productivity

The video introduces Amazon Q, a generative AI assistant designed to enhance enterprise productivity. It discusses how Amazon Q can be customized using enterprise data to solve problems and answer questions similar to a chatbot like Chat GPT. The tool was recently released at the AWS re:Invent conference and is positioned as a knowledge-enhancing service for IT professionals and enterprise workers, capable of mimicking the workflow and configurations specific to an enterprise's needs.

05:00

🛠 Setting Up Amazon Q Services for Problem-Solving

This section provides a step-by-step guide on utilizing Amazon Q services, starting from accessing the service to creating a workspace. It explains the process of naming the application, choosing options, and creating a new workspace. The importance of training the Amazon Q application with enterprise data is highlighted to enable it to answer queries effectively. The video also touches on the use of retrievers for indexing and the integration of data sources like S3 buckets to feed the application with the necessary knowledge base.

10:00

🔌 Integrating Data Sources with Amazon Q for Enhanced Intelligence

The paragraph demonstrates how to integrate various data sources with Amazon Q to make the application intelligent. It covers the process of adding a data source from an S3 bucket and emphasizes the need for clean, refined data to enhance the application's capabilities. The video script also mentions other cloud and on-premises data sources that can be integrated, such as Box, Confluence, GitHub, and Google Drive, to create a comprehensive knowledge base accessible to different roles within an enterprise.

15:01

🔒 Deploying Amazon Q Applications with IDP and SSO Integration

This part of the script focuses on the deployment of Amazon Q applications, which requires integration with an Identity Provider (IDP) for single sign-on (SSO) capabilities. It explains the necessity for enterprises to configure their IDP to deploy the application and make it accessible through SSO. The script also mentions that Amazon Q is designed for large enterprises with their own authentication setups and hints at potential future options for deployment without IDP for smaller entities.

20:03

🗣️ Interacting with Amazon Q: A Preview of the Web Experience

The final paragraph showcases a preview of the web experience with Amazon Q, illustrating how users can interact with the AI once the application is deployed. It describes the interface where users, based on their roles, can ask questions and receive answers from Amazon Q. The script also includes an example of how the AI can respond to basic questions and how it can be trained with a CSV file to provide specific information from the dataset. The video concludes with a request for viewers to subscribe to the channel for more content.

Keywords

💡Amazon Q service

Amazon Q service is a generative AI assistant designed for enterprise use. It is tailored and customized using enterprise data to increase productivity by answering questions and solving problems in a manner similar to chat GPT. In the script, it is highlighted as a tool that can be trained with company-specific data to assist IT professionals and engineers in their daily tasks.

💡Productivity

Productivity in the context of the video refers to the efficiency and effectiveness with which work is done, especially within an enterprise. The Amazon Q service is positioned as a means to enhance productivity by providing quick access to information and solutions, thereby reducing the delays that can occur through traditional collaboration methods.

💡Generative AI

Generative AI is a type of artificial intelligence that can generate new content based on learned patterns. In the video, Amazon Q is described as a generative AI that learns from enterprise data, enabling it to generate responses and solutions to user queries, which is crucial for its role as an AI assistant.

💡Enterprise data

Enterprise data refers to the information and datasets that are specific to a company's operations and workflows. In the script, it is mentioned that Amazon Q is trained with enterprise data, which allows it to understand and respond to company-specific queries and problems, thereby personalizing the AI's assistance.

💡IT Engineer

An IT Engineer is a professional who deals with the design, implementation, and management of computer systems and networks. In the video, the role of an IT Engineer is used as an example to illustrate how Amazon Q can assist in tasks such as VM patching by providing knowledgeable responses based on the data it has been trained with.

💡VM Patching

VM Patching refers to the process of updating or fixing virtual machines (VMs) to improve security and performance. The script uses VM patching as an example of a task that an IT Engineer might need assistance with, and how Amazon Q, once trained with the relevant data, can provide the necessary guidance.

💡Knowledge Base

A Knowledge Base is a collection of information or data from which an AI system can learn and draw upon to provide responses. In the video, the Amazon Q service is described as being trained with a company's knowledge base, which enables it to offer intelligent and contextually relevant answers to queries.

💡Role-based Access

Role-based Access refers to a system of permissions where access to information or resources is granted based on the role of the user within an organization. The script mentions that Amazon Q can personalize interactions based on the user's role, ensuring that they receive relevant information according to their permissions.

💡SSO (Single Sign-On)

SSO is an authentication process that allows a user to access multiple applications with one set of login credentials. In the context of the video, deploying an Amazon Q application for enterprise use may require SSO setup, indicating that it is designed for integration with existing enterprise security protocols.

💡IDP (Identity Provider)

An Identity Provider is a system that offers authentication services for applications. In the script, it is mentioned that deploying an Amazon Q application may require configuring an IDP, which underscores the service's enterprise focus and the need for secure, centralized authentication.

Highlights

Introduction to Amazon Q service, a generative AI assistant designed to enhance enterprise productivity.

Amazon Q can be customized with enterprise data to solve specific business problems.

The service mimics the functionality of chatbots like Chat GPT but tailored to enterprise needs.

Amazon Q was released at the AWS re:Invent conference, showcasing its potential as a knowledgeable tool.

The AI can be trained with enterprise data, allowing workers to access tailored knowledge and solutions.

Example given of an IT engineer using Amazon Q to understand VM patching workflows.

Amazon Q's ability to answer questions directly can reduce delays in IT environments.

The service understands and utilizes company information, code, and systems to provide intelligent responses.

Amazon Q can be personalized based on user roles and permissions within an organization.

Built with security in mind, Amazon Q aligns with AWS's focus on privacy and security.

Demonstration of creating an Amazon Q application called 'developer help'.

Explanation of how to train Amazon Q with enterprise data for specific roles, like developers.

Amazon Q can integrate with various data sources, including S3 buckets and cloud applications.

The process of deploying Amazon Q as a web application for user accessibility.

Amazon Q's potential to become a single source of information for enterprise workers.

Preview of the web experience interface for Amazon Q, showing how users can interact with the AI.

The importance of clean and refined data for training Amazon Q to ensure its intelligence.

Amazon Q's ability to answer generic and specific questions, showcasing its versatility.

Final demonstration of Amazon Q's intelligence after being trained with a sample CSV file.

Call to action for viewers to subscribe to the channel for more informative content.

Transcripts

hey hi in this video we're going to see

how to use Amazon Q service which is

acting as a generative AI assistant for

work so in this video we're going to

show you like you know how the Amazon Q

enables us to to increase the

productivity at Enterprise and we also

try to mimic that you know how that

Amazon queue can be uh you know used to

to help your workers or to help your

professionals who are working in the it

and eventually you know you're going to

see that know there is an increase in

the productivity right so Amazon Q is a

service which is basically released or

are you know uh released in the last uh

ews reinvent I feel this is a very you

know uh very knowledgeable uh tool so

basically this tool can be um you know

can be tailored or customized according

to your Enterprise data with that

Enterprise data this generative AI or

this Amazon Qi gets and knowledge in a

such a way that it will try to you know

solve your problem in the sense it will

try to um you know answer your questions

like how you are asking your questions

in the in the chat GPT right so

similarly you can ask the questions in

terms of the data that is being fed by

know fed with the the data belongs to

your Enterprise in the sense you would

be training this Amazon Q with your

Enterprise data and then when it is

trained you know the your your your

workers or you know your it

professionals can you know take

advantage of those knowledge and try to

solve their problems in the sense that

will actually helps them to is the you

know their job basically so for example

say uh there is a it engineer basically

a cloud engineer working on certain VM

patching right so in that case he don't

know what to do what is this you know

how the you know VM patching workflow is

been set up in that case for example say

if you if you have created an Amazon Q

service application and that application

is been trained with the set of data

that is how your VM patching solution

has been designed how it has been

configured if you can feed this Amazon

que then automatically the another

engineer okay so who is working in the

same uh you know environment can take a

utilization of that in the sense he can

he don't need to come back and ask the

questions he can ask the question

directly to the Amazon q and Amazon Q

will be able to answer you okay so that

is how the powerful it is so basically

this has been recently you know

introduced so in this video what you use

I'm going to watch you through the

further more details about the Amazon Q

right so as you see here currently we

are in Amazon Q page which is actually

been marked as a preview because it is

recently you know released so as I said

you know this is a generative AI powered

assistant designed for the work and that

can be tailored according to the any

Enterprise businesses okay so here if we

go down and see the benefits okay so

basic benefit as I explained earlier

engages in conversation to solve the

problem generate the content and take

the answers you know basically it will

help you to you know help your query in

the sense uh you know when we work in an

uh in an IT environment or in big big

Enterprises you know always there need

to be collaborations okay so you know

and that collaboration lead to a certain

delay which will eventually impact your

productivity right so in such cases

those thin layer know those gaps can be

solved by this you know uh this Amazon

cues potentially okay and understands

your company information code and

systems in the that's what I said

this is a generative AI you know uh

utility that needs to be educated I know

that needs to be fed with a certain data

then this Amazon Q service becomes

intelligent enough to answer your all

your queries it is just like you know

chat GPT right I mean chat GPT in the

sense it is not a readymade to be

consumed but you need to use your own

data train it and then use it like that

yeah then personalizes the interactions

based on your role and permissions yeah

this is the very important fact you know

in the sense like U let's say you have

uh we have created a service called

Amazon Q application called Amazon q and

you have fed with all type of data all

right but certain information is to be

you know uh related to a particular role

in the sense you have a manager you have

a engineer you have a architect right so

these are all different different roles

right and you also have a CEO you have a

CT right so these roles also need to be

segregated in the S the information that

is pertaining to particular role that

that kind of information will be given

to the only those kind of roles in the

sense here we have a you know we have a

rback as well in the sense we have a

role based access as well in the sense

the information that Amazon Q can give

back is can also be controlled with the

with the your rule as well okay that is

also an additional benefit here and

built with the secure in s Yeah by

default any services in AWS are are you

know Incorporated with the secure and

privately all right so that is what the

basic very very basic information about

Amazon Q now I will take you to the real

time you know uh execution okay so here

what I do is I'm going to touch based

upon you know how to use Amazon Q

Services just to mimic the you know the

scenario like you know how an engineer

and know how this Amazon Q service can

enable an Enterprise to solve their

problems in the sense to solve the

problem and eventually increase the know

the uh the productivity yeah all right

so here what I do is I'm I'm in Amazon Q

so you can go to the search button and

type for Amazon Q so you will see this

Amazon queue if you click on that it

will take you to the particular page

like this so currently I'm in Amazon Q

okay so in that one you need to go to

the three bars option click on

applications in the sense you are

actually creating an applications of

Amazon Q So when you say applications of

Amazon Q in the sense we need to create

an an workspace here basically you there

is an option called create workspace

click on that and what I do is I'm going

to give you some some meaningful names

right for example say I know developer

developer help I'll just call it

developer help I'm just just you know

know creating an application called

developer help so as the name says

developer H you know this applications

it is an instance of Genera AI right and

that is made up out of of know Amazon Q

so this application has to be trained

with a set of data that is required for

developers day-to-day work right we will

see what are those I'm just going to

mimic the same scenario with an example

so here let's give an application name

something like this and here choose the

method as it is right and the rest all

options I'm going to click on a create

rest all I'm going to just keep on I

know default right and one more thing is

so it will take a few seconds once it is

created you know it will take us to uh

the next option that is Select Retriever

and uh connect a data source as I said

an instance of Amazon Q application that

I'm creating in this demo is nothing but

it's a hello one it does not has any

data it has a it has a brain but you

know the brain needs to be filled with

the knowledge right so then only you

know the then only he can speak right so

that knowledge is nothing but data

source you know you need to feed this

application instance with the data and

that data is nothing but belongs to your

your company belongs to your Enterprise

okay and then this Amazon Q service

becomes very intelligent and that you

know end point you the that applications

can be accessed by various Engineers who

are working in that particular role and

they can take a help off out of it okay

so let's see how we can do that so in

the retriever we have a use n retriever

we have a use existing retrievers okay

there are multiple options but use net

retrievers are know very efficient one

you can use it so indexing provisioning

so these are like you know you can keep

it in more details you know just explore

the Amazon Q documents and you're going

to understand what are these and then

rest all option I'm going to keep it

default okay let's go to the next option

that is uh know the next next option I'm

just click on next button so here

connected to a data source okay as I

said so if you are trying to um you know

just create this without a data source

nothing but you know this application is

not going to usable you know this this

Amazon Q application is not at all

usable it does not has any knowledge

yeah so you need to make it

knowledgeable so how do you make it

knowledgeable use your Enterprise KBS

knowledge by know knowledge database

yeah this basically KB is nothing but

knowledge database yeah so you need to

use that knowledge Bank train it and

then that could you know that training I

know that kind of training could be U

you know making this application trained

by with your knowledge base I know that

the the actually the the output of that

is nothing but you know your engineers

who are who are actually working in the

organization who are about to come and

work in your organizations take a help

out of it yeah that is what will will be

the that is what I see an immediate

advantages of this application okay so

for now let me just quickly walk you

through the data set you see that sample

data set we can also connect a data set

from the S3 bucket as well okay so you

see that a sample data you can also

upload a sample data here like uh by

clicking on this one

um yeah but for now let's go with the

the other approaches which I have

already uh uh continue adding so just go

back uh uh yeah so just back to the

back okay I'm going to cancel it out

sorry I need to I need to click on the

add data source yeah so here you go we

are on the connect data source here

there are multiple options saying like

popular one is you know you can upload

the data from uh from the S3 bucket web

crawler or you can use the local for

example say know

uh you have a CSC file which contains

the data saying like you know who is so

for example scenario is that you have a

team of 25 people in that one you don't

know who are that right who is that

member what he is doing what you know

that kind of data you can keep it in a

CSC file just upload it here this

becomes trained in the sense this Amazon

Q application BEC trained the another

engineer who is sitting or who is about

to come into your organization and work

can can just query it instead of asking

the member to member in the sense the

application becomes a point of contact

for you to get all the knowledges of

your Enterprise okay that's what the I

see the advantages I'm just taking in a

very very ground level use cases but

there are like n number of use cases

right n number of use cases at n number

of multiple roles belongs to your

organizations right all right so for

example I'm just going to show you like

how you can integrate and with the S3

bucket right so here we can choose an IM

am rule U for example say um create new

recommended one uh then the role number

is this one so we can browse the S3

bucket so uh I have an S3 bucket so in

that three bucket I have kept a CSC file

which I'm going to show you now so you

can upload the file something like this

yeah uh full snc mode you can chose this

one frequency is nothing but run on

demand

yeah all right so click on this create

add data

source which actually adds a data source

so I'm just going to say like you know

so you also need to it's just like you

know you are actually have a a knowledge

base okay so you can tell like uh you

know in an S3 bucket say like you are

keeping a data of your organizations in

an S3 bucket you are keeping the data of

your particular applications in an

particular S3 bucket right you are

keeping the data of your your source

code okay something like that okay you

can you can actually you need to anote

the data sources okay say like here just

say like a test data source

Yeah so basically you know your data has

to be very refined clean data makes you

know make makes this Amazon application

or Amazon Q applications very

intelligent so as just you know by just

adding that tag I'm just going to add

the uh data source which actually what

happens is you know it going to create

IM R it going to sync the data present

in that particular S3 buckets which S3

bucket which I'm going to show you here

so this is the S3 bucket which I'm going

to open it in the another tab so

basically we are using that as a data

source and currently it is creating an

application with using that so here if I

go to this S3 bucket you have the test

report. C file which is nothing but it

has a data in the form this is a CSC

file it has some column names you see

these are the column names and these are

all dummy data yeah so we're going to

see that you know how this Amazon Q

application gets trained automatically

and helps you to solve the you know C

problems okay in this this is a demo

that's reason I'm just mimicking it I'm

not going in a very deeper manner to go

to the deeper manner you know you need

to have a you know further further

requirements which I'm going to show you

right

away all right looks like it has done

I'm going to so not only that I'm going

to so you see that you know um um we

have created an instance of application

uh from from on Q and the name of my

application is develop help right and

and for now you know we have added a

data source which is from from S3 bucket

right all right so there are like

likewise there are any number of data

source could be added as a source of

knowledge here so as I said in the most

popular are from the S3 bucket uh you

see that know there are lots of other

options there are options from the cloud

in the sense if you have a data source

or if you have a knowledge uh you know

bank which is our knowledge base sitting

in the cloud you can also import from

there it does provides multiple other

interfaces you see that it provides box

Confluence you know you can use GitHub

you can use Gmail you can use Google

Drive teams applications yr right so

there are so many Cloud applications

could be used as a source of data to

train this application which could be

used by the the another roles which are

playing a role in a in your company yeah

or in your Enterprise and then go to the

on premises not only Cloud it also

supports a particular on premises

applications as well so on premises

applications it includes you know these

are all the applications you see that

jesk postgress these are nothing but

your databases Yeah so basically uh you

know so when when you are working in the

sense when a particular developer is

working on an application or particular

developer is working on a particular

business case a project manager is

dealing with a particular you know uh

project okay everything everybody

everybody who works in an organization

needs a knowledge needs the information

okay and currently that information the

source of information currently till now

today was like a documents people right

or say like there could be anything

documents database U you know blah blah

things there are so many where are are

like a kind of a kind of a conventional

source of information to do the work but

now you know you have a single point of

you know now the chances that chances

are there that this Amazon Q can become

a single source of information that you

know the peop who are working in

Enterprise can rely on okay so that is

how you know the this this tool has a

capability all about okay so we go to

arm permes okay so likewise you know you

can integrate the the how I'm showed the

um Amazon S3 bucket likewise you can

integrate the the other uh data sources

as well okay now let's go to the uh the

application that we created called

developer help so this is our developer

help application so you can go back

again and click on the

applications currently it is in in

preview mode which is nothing but you

know it is AWS is trying to evaluate you

know how does the user experience is all

about and they try to solve the problems

uh during this phase you know during the

preview phase if there are any problem

they have the AWS has a chance to solve

those problems okay they would like to

hear you from your side as well right

all right so creation of an application

is not enough we need to also need to

deploy you know web experience in the

sense I need to convert this uh into in

the sense this is just an in the sense

we have created a generative AI

application now that generative AI

application to make it consumable we

need to make it it as an web application

right for example say chat

chat GPT in the back end it is a machine

learning big model in the sense it just

like an intelligent chatbot but that

intelligent chatbot is been you know

equipped with front end but nothing but

a page that is a web page through which

user can log in and try to ask the

questions it will try to answer okay

something same experience can also be

done by the Amazon queue on your uh you

know applications to do that you can

click on a web you know deploy web

applications So currently it will ask

you for the choose the authorizations

okay note that you know so since this is

Amazon q and it is only mean for you

know high level Enterprise grade uh you

know U basically it's for Enterprises

who actually has their own IDP and they

have their own SSO setup they have their

own s authentication setup so such cases

you know so in such cases it is possible

to deploy an applications okay so here

it is mandatory to have an IDP then only

you can deploy an application that's

what I see here without that currently

it does not supports okay which I think

that you know maybe AWS tomorrow they

might going to change the another

options in the sense if you don't even

if you don't have um IDP you can still

deploy an application and make it

accessible through the users present in

the AWS account that is a possibility

but it it going to come you know in

above but for now they think that you

know this Amazon Q application is meant

for big siiz Enterprises and big siiz

Enterprises have their own IDP let's

make them appliable in let's make them

accessible over the SSO only and that's

the reason it shows you configure your

IDP and then once your IDP uh is been

configured you will get a metadata you

need to upload the metadata and provide

the attributes here and click on a

deploy it will try to deploy an

application it will expose in the sense

this uh you know the um Amazon Q

application gets exposed over the uh

particular web app and from the web app

you know you can try to uh try to see

the experience okay so now I cannot show

that because I don't have any IDP

systems that I can configure and deploy

an application for now but I can try to

mimic the scenarios with using preview

web experience okay if you click on a

preview web experience so this is how

your web you know web experience will be

once this application is deployed with

using your IDP right so this is how the

interface will look like because this is

awesome right for example say I'm an

engineer I'm an architect I'm an you

know developer okay so what I do is I

need to get some data to perform my job

I will be given with this interface and

a role is assigned within on this

interface now I just cannot I just have

to question I just have to ask the

questions to Amazon Q right and

automatically it will it will give me

the answers that is cool right that is

actually very cool okay so earlier in

traditional days we would be assigned

with a hell lot of documents we will be

assigned with a buddy who is never going

to help us right and and you know so

there is no basically there will be a

collective knowledge sharing sessions

will be happening but there are like you

know those are like very very very

basically bifurcated or lots of human

problems right but now you don't have

that you know this says that barrier is

removed you know you can directly jump

and ask the question and try to do your

job okay that's what it happens now what

I do is you know I will just ask you

know questions like how are you I'm just

trying to see you know how does this

behave let's see whether it going to

reply to the uh you know what does it

repli okay let's see I'm just saying how

are you it says thank you I'm doing so

basically this Amazon Q uh you know uh

Genera AI tool it has a basic

intelligence to respond to certain uh

information but you know so you see that

you know I'm doing well thank you you

for asking I'm Amazon Q and A assistant

created by Amazon web services how can

help you right so I will tell I will

tell what is the what is today's date

yeah what is today's date so these are

all the information that you don't need

to ask your colleagues you know when

you're are working but this is in a very

generic information so basically what

I'm why I'm this asking these kind of

questions in this um um in this uh

preview experience is that I just wanted

to get you a confident that you know yes

this is a chart bot kind of thing yeah

or basically it's a intelligent chatbot

ta for particular you know uh Enterprise

okay so let me ask you know more details

get me more

details about my ARG if I ask this

question it will not answer why because

basically know I have not trained this

an application with the required data

you see that you know I'm afraid that I

don't have a details of your

organizations okay but to mimic that

scenario what I do is I'm going to feed

this with a an example data which I

already have okay so what I do is I'm

going to before I show you this So

currently I have integrated with the S3

bucket but the S3 integration with the S

S3 bucket will not still not work

because to make that working I have to

deploy the application okay that's the

reason it is not working so for now what

I do is I have downloaded one example

file called CSV file so this is a CSC

file is nothing but you know this is my

application data this is my application

database storage what I do is I'm going

to I'm going to upload this into that

particular application and try to ask

more details about this particular

application okay particular user okay

let's see so what I do is I'm going to

I'm going to train that generate AI in

the sense I'm going to upload this data

into the generate Ai and ask a questions

about the about the data yeah ask a

questions belongs to that particular

data set okay so what I do is I'm going

to go to downloads select this CSC file

and what you do is you going to ask here

saying like you know um give

more

details about email this way so if I

type this question what it does is it is

actually reading it is actually going

through a result. CSC file it is getting

educated itself and it is actually

giving me the response you see that you

know the email is belongs to this guy

and from the Uganda in the sense his

place is from the Uganda according to

the results in the sense according to

this database it got some knowledge in

the sense it got the knowledge saying

like you know hey there is these are the

datas I know he works as administrative

privilege you know professional at

Uganda communic okay you see that you

know how how intelligent this Amazon Q

is all about yeah all right so basically

um um you know so likewise you know you

can ask many other questions okay tell

me I will tell you tell me explain me E2

instance I will just ask explain me more

about

E2 I'm just asking very generic you know

general knowledge questions about uh AWS

okay so I'm just asking um uh basically

explain me more about

E2 right so let's see you know how does

it response explain me more about E2 let

me see you know if it can answer okay so

basically you see that I'm sorry I could

not find the relevant data okay so what

I mean to say is that you know so this

result. CSV is an example of knowledge

base right that you could that you could

train this application that is Amazon Q

application and make your engineer you

know you know enabled with the lots of

information so that they can do their

work you know very effective way and

they become more productive here okay

all right so with that note you know I

have shown you the things need to be

shown in this video finally a kind

request please do subscribe my channel

that would really encourage me a lot

with that note thank you thanks a lot

and see you in the next video