The content explores the development and deployment of specialized chatbots, moving beyond general-purpose bots to address specific business needs, particularly in customer service, by integrating them with human agents in various capacities.
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in the last two videos we looked at
writing and reading applications in this
video we'll look at chatting
applications in addition to the general
purpose chat boot like chat bot and ban
chat many companies are looking at
whether they can build specialized chat
applications if you're involved in a
company where you have many people
interacting with customers or having
certain types of conversations of
similar nature this may be a case where
you can consider what or Not A
specialized chat bot can help with those
types of conversations let's take a look
earlier we already saw the example of a
customer service chatbot that might be
able to take orders for cheeseburger
another example of a specialized chatbot
would be one that specializes in helping
you to plan trips so how can vacation in
Paris inexpensively and a bot could be
built to have specialized knowledge
about travel and today there are
companies exploring a wide range of
advice Bots for example can a bot give
you career coaching advice or give
advice on cooking a meal so a large
variety of specialized Bots that are
really good at answering questions about
one thing are being developed by
different companies today some of these
BS are capable of just having a
conversation and giving advice some of
these BS can also interface with the
rest of a company Software System and
take actions such as to put in an order
for a cheeseburger to be delivered
another example of a bot that might bu
take action would be a customer service
chatbot where it turns out that many it
departments get tons of password reset
requests and if a bot can take care of
that then it may take some of the
workload off your it department and a
bot like this that needs to be send a
text message to verify identity and
actually hope reset a password this is a
bot that would need to be empowered to
actually take action in the world such
as to get a text message to be sent to
someone next week we'll discuss more how
chat bars like these are built that
don't just generate text but can
actually take action because of the
number of customer service organizations
exploring the use of chat bot I want to
share with you a range of the spectrum
of common design points being used by
different businesses and for this slide
I want to focus on text based chat
rather than Voice or phone based chat
so at one end of the spectrum would be a
customer service center with only humans
so you would have Human Service agents
typing back and forth messages like
welcome to P burgers and let me play the
order for you at the opposite end of the
spectrum would be chat Bots only where
you just have software responding
directly to customers but between these
two ends of the spectrum of humans
typing at the keyboard or chat BS only
there are a couple common design points
one common design points would be to
have Bots support humans in which a bot
will generate a suggested message for
human but the Human Service agent will
read the message and either approve it
if it looks good or have a chance to
edit the message before it is actually
sent back to the customer this type of
design is often also called human in the
loop because as's a human that's looped
in and is part of the process before the
message actually gets sent back to your
customer and this is a way to mitigate
the risk of the chat bot maybe saying
the wrong thing because a human can
check over it before it's actually sent
back to your customer in the next video
when we talk about what lm's can and
cannot do we'll go over some of the
mistakes that LM can sometimes make and
so this design helps protect against
those mistakes of LMS a little bit
further on the automation Spectrum would
be if you have a bot triage messages for
humans so maybe the bot answer the easy
messages but escalate to a human for the
things that isn't quite ready to handle
yet sometime back I actually L A team
that build a bot that would
automatically detect if the customer was
asking for refund requests it turns out
that was about 10% of our total chat
call volume and by just detecting that
and automatically giving the customer
instructions this routed 10% or so of
the traffic away from the human agents
and so to save the agents a lot of time
and let the humans focus on servicing
the harder requests but this type of
triaging is another common design to
help your Human Service agents save time
and have to focus only on the harder
cases that they're more uniquely
qualified to handle in many customer
service centers a single human may be
simultaneously having chat conversations
with four or eight or in some extreme
cases maybe even 16 customers at the
same time and with B supporting the
humans it becomes easier for a human to
manage a a larger number of parallel
conversations given that Bots sometimes
say the wrong thing I want to share with
you what building and deploying a bot
often feels like in companies that want
to do this in a safe way often companies
will start with an internal facing chat
bot so many times I would build a chat
bot but let only my own team use it um
to say answer the questions about travel
or whatever the bot is supposed to do
and assuming your internal team will be
more sympathetic and more understanding
of mistakes and be more forgiving if the
bot says something wrong that one time
this gives you some time to assess the
behavior of the bot and also avoid
public mistakes that could be
embarrassing for the company after this
looks safe enough a common Next Step
would be to deploy with human in the
loop to let a human check over many of
the messages if feasible before it
actually goes out to the customer and
after doing this for a while if it looks
like the bot's messages are are
generally safe to send to customers then
you might allow the bot to communicate
directly with customers of course the
details of every business defers and for
some applications it may not be
practical to have humans check over
every message because of the Shar volume
of traffic but depending on the risk of
the bot saying the wrong thing as well
as the volume of traffic and thus
whether or not human in the loop is
feasible these are some of the design
patterns I've seen company used to try
to deploy Bots safely to summarize we've
seen how LMS can be used for writing
reading and chatting these three
categories are not meant to be an
exhaustive list of what LS can do but
there just a few broad categories of
what you might really use them for and
LS can do a lot but they can't do
everything in the next video let's take
a look at what LS can and cannot do and
better understand the limitations let's
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