My teams have worked with or advised many companies,
both large and small,
on building a large variety
of generative AI 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 a project might look like.
The most common roles for building
generative AI applications would be
a software engineer who would be responsible for
writing the software application
and making sure that it runs robustly.
I've seen that when a software engineer puts in
a bit of effort to learn
at least the basics of
large language models and prompting,
then they can be very effective in
a small team building LLM-based applications.
If you're on a team that
already has some software engineers,
it might be worth encouraging them to consider spending
a little bit of time to learn
at least the basics of LLMs and prompting.
A second role 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 Gen AI took
off and found that a machine learning engineer
that spends a bit of
effort to learn about LLMs and ideally not prompting,
but some of the more advanced techniques
like RAG and fine-tuning,
such a person can be very effective,
playing a role building LLM applications.
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 prompt 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 those as a dedicated role.
What happened was a small number of companies
advertised a small number of job openings for
very well-paid prompt engineers and 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 prompt engineers,
prompt engineering jobs actually
require doing a lot of other tasks than writing
prompts and they actually look more like
machine learning engineers that
have additionally learned to prompt.
Don't buy into hype prompt engineer role.
What actually happens in practice is that
most companies are counting on machine learning engineers
that have also learned LLMs or learned
prompting and it's actually not
that easy to get a job and no other company's hiring
that many people whose only job is to write prompts.
If you're building an LLM-based application,
it's often possible to get
started with a pretty small team.
I definitely see companies start to
experiment with even a one-person team,
such as a software engineer
who has learned some prompting.
Or machine learning engineer
who's learned a bit about prompting OMs.
Or maybe you could 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 two-person teams as well.
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 learned prompting and a product manager,
or two generally enthusiastic
people that know a bit about how to write
software and they're willing to learn how to use
these tools to build new and 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 a company.
Data engineer is usually
responsible for organizing the data
and ensuring data quality and
often also the security of the data.
A 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.
Generative AI has lowered the cost,
lowered barrier to entry
to building AI-based applications.
If your team have an idea,
I'd encourage you to try to find
the 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 generative 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 that in the next video.