Generative AI Powered Assistant At Work | Amazon Q Service | AI | Generative AI | AWS
Summary
TLDRThis 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
🚀 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.
🛠 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.
🔌 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.
🔒 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.
🗣️ 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.
Mindmap
Keywords
💡Amazon Q service
💡Productivity
💡Generative AI
💡Enterprise data
💡IT Engineer
💡VM Patching
💡Knowledge Base
💡Role-based Access
💡SSO (Single Sign-On)
💡IDP (Identity Provider)
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
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