Building generative AI applications can be achieved with relatively small, focused teams, primarily leveraging software and machine learning engineers with foundational knowledge in LLMs and prompting, rather than relying on specialized, hyped roles like dedicated prompt engineers.
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my teams have worked with or advised
many companies both large and small on
building a large variety of geni
applications I'd like to share with you
in this video some of the best practices
I'm seeing as well as what the typical
team to get started on such project
might look like the most common roles
for building gentv applications would be
a software engineer who would be
responsible for writing the software
application and making sure that it runs robust
robust
and I've seen that when a software
engineer puts in just a bit of effort to
learn at least the basics of large
language models in prompting then they
can be very effective in a small team
building L based applications so if
you're on a team that already has some
software Engineers it might be worth
encouraging them to consider spending
just a little bit of time to learn at
least the basics of Ls and prompting a
second row that is quite common on teams
to build applications would be the
machine learning engineer and machine
learning Engineers are typically
responsible for implementing the AI
system many machine learning Engineers
have been building AI systems even
before generative AI took off and I
found that a machine learning engineer
that spends just a bit of effort to
learn about LMS and ideally not just
prompting but some of the more Advanced
Techniques like Rag and fine-tuning such
a person can be very effective playing a
role building applications and finally
one other role that I see in some teams
but less common than the software
engineer and machine learning engineer
would be the product manager and they
would be the person with primary
responsibility for identifying and
scoping the project and making sure that
whatever is built is useful for
customers lastly how about the promt
engineer role there's been a bit of
media hype about this role but what I'm
seeing is that very few companies are
hiring this as a dedicated role what
happened was a small number of companies
advertised a small number of job
openings for very well-paid prom
engineers and this generated a lot of
media hype that someone could make a lot
of money by prompting but if you look at
the actual job description of prom
Engineers promt engineering jobs
actually require doing a lot of other
tasks than just writing prompts and they
actually look more like machine learning
Engineers that have additionally learned
to prompt so don't bind the hype of the
prompt engineer Ro
what actually happens in practice is
that most companies are counting on
machine learning Engineers that have
also learned LS or learned prompting and
is actually not that easy to get a job
and nor company is hiring that many
people whose only job is the right
prompts if you're building an L based
application it's often possible to get
started with a pretty small team so I
definitely see companies start to
experiment with even just a one person
team such as a software engineer
engineer who's learned some prompting or
a machine learning engineer who's
learned a bit about prompting in alms or
maybe you could just start by yourself
by experimenting and prototyping using
some of the web interfaces to try to get
a sense of what might be feasible I do
see a lot of TW person teams as well and
if you have two persons in a team
probably the most common configuration
is a machine learning engineer plus a
software engineer but I've seen many
other configurations also work well such
as a software engineer who's learn
prompting and a product manager or
really to generally enthusiastic people
that know a bit about how to write
software and are willing to learn how to
use these tools to build new and exciting
exciting
applications sometimes for the larger
teams you also see some other roles like
data engineer data scientist project
manager or machine learning researcher
let me quickly talk over these roles as
well in case you see them in the company
so data engineer is usually responsible
for organizing the data and ensuring
data quality and often also the security
of the data and data scientist is
usually responsible for analyzing data
to make recommendations to guide project
or business decisions project manager
can be responsible for coordinating
project execution and machine learning
researchers are usually responsible for
developing Advanced AI Technologies or
adapting Advanced AI Technologies to the
particulars of your business so G of AI
has lowered the cost lower the barrier
to entry to building AI based
applications if you or your team have an
idea I'd encourage you to try to find a
resources to prototype and try something
out and see if you can build something
for yourself or for your business before
we wrap up the section on gen of AI and
businesses I'd like to go through an
analysis of how AI is affecting
different job roles as well as different
industry sectors let's take a look at
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