Will Prompt Engineering Replace Coding?

Summary

The Nvidia GTC conference highlighted the emergence of prompt engineering as a new coding paradigm, showcased by the Nvidia inference micros service. This development emphasizes the increasing abstraction in software development, from assembly code to high-level languages, and now AI-driven solutions. The video discusses the evolution of programming and the importance of understanding underlying engineering principles, critical thinking, and problem-solving in the tech industry, predicting a future where life sciences will transition from discovery to engineering.

Takeaways

• 🚀 **Prompt Engineering's Emergence**: Nvidia's CEO, Jensen Hong, introduced the concept of prompt engineering as the new coding at the GTC conference, emphasizing the minimal code required to build AI applications through the Nvidia Inference Micros service.
• 🧬 **Healthcare Application**: An example given in the transcript illustrates how prompt engineering can be used in healthcare to analyze cancer genetics and imaging data, potentially leading to the development of targeted drugs in minutes.
• 📈 **Benchmarking with AI**: The transcript introduces 'Devon', an AI software engineer, which benchmarks the performance of an AI model on different API providers, showcasing the capabilities of prompt engineering in real-time.
• 🌐 **The Evolution of Coding**: A historical overview of programming languages from the 1940s to the present is provided, highlighting the trend of increasing abstraction and simplification in coding practices.
• 📚 **Educational Relevance**: The importance of understanding low-level programming languages in education is emphasized, as it provides a foundation for grasping engineering principles and problem-solving skills.
• 🛠️ **Software Engineering Principles**: The distinction between coding and engineering is made clear, with the latter involving critical thinking, problem-solving, and the application of engineering principles to build complex software systems.
• 🤖 **AI and Engineering**: Jensen Hong suggests that the future will see the application of engineering principles to fields like life sciences and drug discovery, moving from scientific discovery to engineering solutions.
• 📈 **The Role of AI in Various Fields**: The transcript discusses the potential for AI to impact not just technology but also other sectors like healthcare, climate change, and life sciences, through the application of data analytics and engineering principles.
• 💡 **The Importance of Understanding Outputs**: It is highlighted that understanding the desired output and the engineering principles behind achieving it is crucial, even as coding becomes more abstracted and accessible.
• 🔍 **Research and Development**: The transcript touches on the importance of R&D in both technology and life sciences, suggesting that the future may see more integration between these fields through engineering principles.
• 🎯 **Focus on Productivity and Problem-Solving**: The ultimate goal of prompt engineering and AI tools is to enhance productivity and problem-solving capabilities, as exemplified by the use of AI like Devon for benchmarking and analysis tasks.

Q & A

What is the main idea Jensen presented at the Nvidia GTC conference?

Jensen introduced the concept that prompt engineering is the new coding, emphasizing the efficiency and minimal coding required to build and deploy AI applications through the use of Nvidia's inference micros service or NIM.

What is prompt engineering, and how does it differ from traditional coding?

Prompt engineering is the process of guiding generative AI to produce desired outputs with minimal coding. It differs from traditional coding, which involves creating detailed instructions for computers to follow, by abstracting much of the complexity and allowing users to achieve goals with fewer lines of code.

How did the field of programming evolve from the 1940s to the 2020s?

Programming evolved from complex, low-level languages like assembly code to high-level, abstracted languages and frameworks like Python, Ruby, and TensorFlow. This evolution made coding simpler, faster, and more accessible, with a continuous trend towards higher-level abstractions and easier development processes.

What is the significance of Margaret Hamilton's contribution to software engineering?

Margaret Hamilton was a pioneer in software engineering, playing a crucial role in the Apollo moon missions and coining the term 'software engineering.' Her work emphasized the importance of rigorous engineering principles in creating complex software systems.

What is Jensen's perspective on the future of computer science and life sciences?

Jensen envisions a future where life sciences will transition from a field of scientific discovery to one of engineering, with digital biology and drug discovery becoming part of life engineering. He encourages learning about human biology and engineering principles to contribute to this emerging field.

How does the script relate the historical development of programming languages to the emergence of prompt engineering?

The script draws a parallel between the historical simplification of programming languages and the emergence of prompt engineering, suggesting that prompt engineering is a continuation of the trend towards more accessible and efficient computation processes.

What is the role of prompt engineering in the development of AI products and tools?

Prompt engineering is seen as a critical skill for developing AI products and tools, as it allows for the efficient creation and deployment of AI applications across various domains with minimal coding, thus increasing productivity and innovation.

How does the script suggest we should approach learning and applying AI technologies?

The script encourages learning prompt engineering as a way to leverage AI effectively. It suggests using resources like HubSpot's chat GPT bundle to practice and incorporate AI into work and life, emphasizing the importance of understanding the principles behind AI and engineering rather than just the coding aspect.

What is the significance of understanding the underlying engineering principles in software development?

Understanding the underlying engineering principles is crucial because it allows developers to think critically, solve problems effectively, and build complex software systems efficiently. It also helps in quickly grasping new technologies and adapting to the evolving landscape of software development.

How does the script address the concern that prompt engineering might replace traditional coding?

The script acknowledges that prompt engineering may reduce the need for traditional coding skills but emphasizes that the fundamental principles of data analytics, engineering, and problem-solving will remain essential. It suggests that the ability to understand and apply these principles to achieve desired outcomes is irreplaceable.

What advice does Jensen give for someone starting their education anew?

Jensen advises focusing on understanding the complexities of human biology and embracing the field of life sciences, as he believes that the future lies in life engineering, which includes digital biology and drug engineering, transforming life sciences into a more engineering-focused discipline.

Outlines

00:00

🚀 The Emergence of Prompt Engineering and AI Software Engineers

This paragraph discusses the Nvidia GTC conference where CEO Jensen Hong introduced the concept of prompt engineering as the new coding. He showcased the Nvidia Inference Microservice (NIM) that simplifies AI application development through minimal coding. The example of a healthcare company leveraging AI to analyze cancer data and devise targeted drugs is provided. The narrative also touches on the historical evolution of programming, from the early days of assembly code to the advent of high-level languages like Python and JavaScript, emphasizing the continuous abstraction and simplification of coding processes over the decades.

05:02

🤖 Introduction to Chat GPT and Prompt Engineering

The second paragraph introduces Chat GPT as a tool for practicing prompt engineering. It highlights the availability of a comprehensive Chat GPT bundle by HubSpot, which contains numerous prompts for users to experiment with. The paragraph discusses the utility of Chat GPT in information gathering, virtual assistance, and email automation. It emphasizes the free nature of the HubSpot bundle and its potential to enhance productivity. The video then transitions back to the main topic, defining prompt engineering and comparing it with traditional coding, noting that both involve input, processing, and output, but with prompt engineering being more streamlined and efficient.

10:03

🌟 The Evolution of Coding and the Role of Engineering Principles

This section delves into the personal experience of the speaker as a student and a software engineer, reflecting on the importance of understanding low-level programming languages like assembly code. It argues that this foundational knowledge is crucial for grasping engineering principles, which are the bedrock of software development, rather than just coding. The paragraph also discusses the broader implications of prompt engineering, suggesting that while it may simplify the coding process, the underlying engineering principles remain essential. These principles include data analytics, iterative problem-solving, and critical thinking, which are vital for defining and achieving desired outcomes in technology and other fields.

15:05

🌐 The Future of Life Sciences and Engineering

The final paragraph shifts focus to the future of life sciences, highlighting Jensen Hong's perspective on the field's transition from a scientific to an engineering discipline. The speaker discusses the potential of digital biology and life engineering, drawing parallels with their own background in pharmacology and computer science. The video concludes with a call to action for engineers, data scientists, and technical professionals to apply their expertise in solving complex problems in life sciences, drug discovery, and addressing global challenges like climate change. The speaker expresses hope for a new generation of individuals who will drive innovation in these areas, concluding the video on a forward-looking and optimistic note.

Keywords

💡Prompt Engineering

Prompt engineering is a method of guiding AI systems to generate desired outputs through the use of prompts. In the context of the video, it is presented as a new paradigm of coding, where traditional programming is seen as less necessary. The video illustrates this by showing how, with prompt engineering, tasks that would require extensive coding can be accomplished with minimal effort, such as deploying an AI application for cancer analysis in healthcare using just a few lines of code.

💡AI Software Engineer

An AI software engineer is a professional who specializes in the development, design, and maintenance of software applications that utilize artificial intelligence. In the video, the concept of AI software engineers is introduced with the example of Devon, an AI entity capable of benchmarking performance on different API providers, signifying a shift towards AI's role in software engineering tasks.

💡API Providers

API, or Application Programming Interface, providers are entities that supply the protocols and tools for building software applications. These providers enable different software to interact with each other. In the video, the concept is used to discuss the benchmarking of AI models across various API providers, highlighting the importance of understanding and utilizing these services in modern software development.

💡Coding

Coding is the process of creating instructions that computers can interpret and execute. It is a fundamental aspect of software development and has evolved significantly over the years. The video discusses the transition from traditional coding to prompt engineering, suggesting that the latter may simplify and streamline the development process.

💡Software Engineering

Software engineering is the application of engineering principles to software design, development, testing, and maintenance. It involves creating efficient, reliable, and scalable software solutions. In the video, the importance of software engineering principles is emphasized, even as prompt engineering emerges, to highlight that the foundational concepts behind building software remain crucial.

Highlights

Jensen Huang, Nvidia's CEO, introduced the concept of prompt engineering as the new coding at the GTC conference.

Nvidia's inference micros, service or NIM, simplifies AI application development through prompt engineering, requiring minimal coding.

Healthcare companies can utilize AI applications to analyze cancer genetics and imaging data, potentially discovering targeted drugs in minutes.

The notion that traditional coding may become obsolete, with prompt engineering taking its place, was discussed by Jensen Huang.

Devon, the first AI software engineer, was introduced and demonstrated benchmarking the LLM, LLa3 on different API providers.

The historical development of programming languages, from assembly code to high-level languages like Python and JavaScript, shows a trend of simplification and abstraction.

Margaret Hamilton's contributions to software engineering and the Apollo 11 moon landing were mentioned, emphasizing the importance of engineering principles.

The evolution from low-level programming languages to high-level languages and frameworks has made software development more accessible and efficient.

Prompt engineering and traditional coding share a similar process of input, processing, and output, but with less complexity in prompt engineering.

The importance of understanding the underlying engineering principles, even when using high-level languages and tools, was emphasized for effective problem-solving and critical thinking.

Jensen Huang suggests that the future may see the field of life sciences transform into life engineering, with digital biology becoming a key area of focus.

The potential for AI and engineering principles to revolutionize drug discovery and life sciences was discussed, moving from scientific discovery to engineered solutions.

The importance of asking the right questions and understanding the desired outcomes in engineering and data science was highlighted.

The video encourages viewers to embrace the power of AI and learn prompt engineering to increase productivity and develop AI products and tools.

HubSpot's chat GPT bundle was mentioned as a resource for practicing prompt engineering, offering pre-made prompts for various applications.

The video concludes with a call for technical professionals to use their expertise in engineering solutions for life sciences, drug discovery, and addressing global challenges like climate change.

Transcripts

in the Nvidia GTC conference their CEO

Jensen Hong demonstrated how prompt

engineering is the new coding he

introduced the Nvidia inference micros

service or Nim that basically bundles

together all the software that you need

in order to build and deploy an AI

application in any domain that you want

mostly through prompt engineering you

only need like three lines of code or

less for example if you're a Healthcare

company and you have a lot of data on

cancer genetics and imaging you can

deploy an AI application that's able to

analyze all of the data that you have

and come up with novel targeted drugs

for specific types of cancer in

literally minutes and what's interesting

is that just a few weeks back in a

conference he said it is our job to

create Computing technology such that

nobody has the program basically coding

is dead and the future is impr prompt

engineering now couple this with Devon

and today I'm really excited to

introduce you to Devon the first AI

software engineer and here is Devon

being prompted to Benchmark the llm

llama 3 on three different types of API

providers I'm going to ask Deon to

Benchmark the performance of llama on a

couple different API

providers from now on Devon is in the

driver's seat we'll come back to this

example later because the prompt itself

over here holds the key to answering the

question is prompt engineering truly the

new coding you will leave this video

understanding what Jensen means by

nobody will need to learn coding anymore

and what you should be focusing your

attention on

instead

in 1843 aah Lovel wrote algorithms for a

mechanical General purposed computer

she's often celebrated as the first

computer programmer now mve to the mid

20th century people usually call this

the birth of modern programming looking

forward 20 years I'm quite certain that

the coming of the computer will have a

significant effect on all businesses and

most private lives during World War II

the first electrical computers were

developed such as the Colossus which is

used by British code Breakers and the

eniac in the United States this time

period was also an assembly code

introduced so you no longer have to do

the 0101 01s this development made

coding so much faster and there was

quickly other more higher level

languages that were developed such as

Fortran in 1957 which is developed by

IBM and Cobalt which was developed in

1959 for business data processing as

well as lisp for artificial intelligence

research contrary to what a lot of

people think that AI was something that

is like this new fangle thing actually

AI research started in the 1960s at this

point coding was a lot easier and faster

but compared to modern times it's not

even close for example this is Margaret

Hamilton standing next to the kesir RO

to get a poloo to the Moon she was also

the first one that came up with the term

of software engineering keep that in

mind this is very important and we'll

come back to this later but first let's

finish our history lesson the 1970s saw

the develop velopment of an even higher

level programming language called SE

this is a language that's now able to

support complex data structures and

algorithms which greatly facilitated the

development of very complex large

software systems moving into the end of

the century the 1980s saw the rise of a

lot of software engineering principles

which was quickly adopted with the Avent

of C++ and inherently an objectoriented

programming language and of course

finishing off the century is when the

internet came about but it's very hip to

be on the internet right now this led to

a need for languages that supported web

development starting off with HTML CSS

and JavaScript in order to enable

Dynamic web content finally going into

the 21st century everybody was on the

internet and these things called

smartphones became very popular so did

mobile development this of course led to

a development of lots of different new

languages as well as Frameworks that can

make sure that the software is more

scalable more safe and faster to write

these languages and Frameworks became

more and more abstracted away in high

level like pyth on Ruby react and tensor

flow for machine learning a lot of

people who use these highlevel languages

genuinely don't even know about how

memory management works which was like a

huge thing just a decade ago but yes

finally now going into the 2020s the

decade that we live in now this trend of

things being more and more abstracted

weight does continue with so much data

that's being collected there's now a

huge emphasis artificial intelligence

where machine learning as well as data

science developer tools and environments

such as docker kubernetes and

cloud-based platforms are becoming more

and more popular as we build and run

more and more complex software as you

can see the trend that has been going on

since the 1940s is that you have

something super tedious and somebody

comes along and abstracts some of these

Concepts and processes to make coding

simpler and faster and then somebody

comes along again and does the same

thing to abstract more things away so it

becomes simpler and faster so on and so

forth regardless of how prompt

engineering and traditional coding

intermingle and develop moving into the

future I think we can all agree that

prompt engineering is a must-learn skill

in order to be more productive in life

and in work and eventually be able to

develop AI products and tools the

easiest way to start practicing prompt

engineering is through chat gbt luckily

you don't need to figure things out

yourself from scratch HubSpot has a full

chat GPT bundle that contains hundreds

of proms that you can start using to

practice prompt engineering the

resources are really well laid out and

they give you lots of ideas on how you

can incorporate Chachi BT into your work

and your life to make you much more

productive for example have you ever had

to gather a lot of information and read

through a lot of information to prepare

for a presentation or a report Chach p

is actually really good at this it's

able to consolidate a lot of different

articles tutorials and different

resources it can be your virtual

assistant your personal conci your

writing assistant and so much more my

favorite High Roi section is being able

to automate my emails and to be able to

filter through news because there's just

way too much news out there and not

enough time the best part of this bundle

it is completely free you can download

it at add this link over here also

linked in description thank you so much

HubSpot for providing these free

resources in order to help us Leverage

The Power of AI and for sponsoring this

portion of the video now back to the

video

engineering engineering come in prompt

engineering is defined as the process

where you guide generative artificial

intelligence generative AI solutions to

generate desired outputs like for

example you can ask Devon to come up

with a benchmark comparison for llama

being run on three different API

providers then comes up with these

comparison numbers and even makes a nice

little graph that gets deployed now

let's compare that to a definition of

coding which is the process of creating

instructions that computers then

interpret and follow to do the same

thing of benchmarking llama on three

different API providers you would use a

highlevel modern programming language

such as python or some flavor of

JavaScript you need to read a

documentation to figure out exactly how

to use these apis run the code to run

Lama the large language model record the

time it takes then explicit create that

visualization and deploy it while

probably wasting a lot of time just

debugging things because things rarely

work the first time as you can see to

accomplish the same task coding is a lot

more complex and a lot more timec

consuming but what I want to point out

is that prompt engineering and coding

using python or JavaScript or whatever

language actually still follow the same

process first you have some input or

instruction whether that be through a

prompt or code then you have some sort

of processing that happens whether that

be within in artificial intelligence or

directly to the computer itself and

finally you get this desired output of

that nice little visualization that got

deployed so you see from Modern Day

encoding to prompt engineering it

actually follows that same Trend since

the 1840s you have some sort of

computation process to get the output

that you want but someone comes along to

make this tedious process a little bit

easier by abstracting away some things

there still following that similar

process of having some sort of input

some sort of computation processing that

happens and then your desired output at

the end so it really does seem like

prompt engineering is just the evolution

of coding doesn't

it since grad school when I was doing my

degree in a University of Pennsylvania

one of the first classes that I had to

take which forces you to start from the

lowest level programming language of

assembly code then moving up to C and

C++ Java and then python I still

remember wanting to JMP out of the

window when it took 30 lines of code

just to write a for Loop if you know you

know basically if you don't that's like

the command or function that you have to

use in order to go from one line of code

to some other line of code anyways it

was an awful experience and I have

definitely had times when I have burst

into tears and I had no idea why they

put us through this because who still

programs an assembly code and for most

people now even C or C++ were even Java

but you see after I graduated and got a

job as a software engineer I realized

that that was one of the most important

courses I have ever taken because from

forcing me to program at such a lowlevel

language and then going upwards I was

able to understand what was going on

beneath the hood and the concepts

engineering principles that's been

abstracted away these days as well as

how to think critically build things and

continue coding despite tears streaming

down my face these concepts of

engineering principles is now why I'm

able to learn languages so quickly to

grasp new technologies and learn how to

use them very very quickly you see

there's a difference between coding and

Engineering coding is just the way that

you're getting a computer to do certain

things but engineering is figuring out

what it is and what's the best way to

build the thing that you want to build

going back to Margaret Hamilton from

earlier whose contributions led to the

successful Landing of AP poloo she was

also the one that came up with the term

of software engineering you see before

that we were implementing pretty simple

things like just counting things doing

some addition processing some things

like summing up some numbers doing some

simple calculations but as the demand

for computation increased and more and

more high level coding languages came

about we were able to write more complex

pieces of software that call for more

engineering principles on how it is that

you should be building software so that

it's fast accurate scalable allows you

to work in parallel with other Engineers

so it's not just the language itself

that's being more abstracted away and

easier to use these highlevel

engineering principles were also being

developed Concepts like data structures

algorithms objectoriented programming

containerization these are all

fundamental developments and huge

Paradigm shifts for how we think about

and develop software coding is simply

the language we use in order to get the

computer to implement these engineering

principles to build the things that we

want to build now going back to this

prompt itself to Deon I'm going to ask

Devon to Benchmark the performance of

llama and a couple different API

providers if you break it into pieces

the first part is hey Devin I'll like

for you to Benchmark llama 2 on three

different providers second part is

replicate together in perplexity third

part is figure out their API formats and

the fourth part is write a script that

sends the same prompt slpms to all of

them okay so if you have absolutely no

understanding of engineering or how any

of these computery things work first of

all how would you even have known that

it's an important thing to Benchmark

llama on different providers why are

there even different providers why is

there differences between them why do I

care if there's any differences then of

course you need to know what are these

different providers for figuring out

their API formats if you like don't know

what an API is like the concept of

software interacting with other software

even if it doesn't matter how it is that

you can pull from an API still how would

you have like come up with this prompt

then script what is script why does it

matter how it is that I'm sending the

same parameters or the prompts uh to

these providers assuming again that I

even know to care about the speeds of

these different providers before y'all

come after me and go like oh like these

things eventually can be simplified as

well well so you don't have to

explicitly write things no no no I'm not

saying that it won't I personally

actually don't doubt that promt

engineering will become better and

better and replace coding in the

traditional sense I agree that everybody

can become a programmer at some point

that you don't need to learn specific

languages like C Java python but what

won't get replaced is the principles

that govern data analytics data science

and engineering being able to

iteratively figure out what needs to be

changed in order to get a better output

figure out what even is the output that

you want you'll be surprised by how many

non-technical people genuinely don't

know what they want like two-thirds of

my job at meta is going like hm is that

really what you want if you're a

software engineer data analyst data

scientist ml engineer AI engineer

whatever some sort of technical person

what you do isn't just coding it's

figuring out the right question

understanding the appropriate solution

and breaking it down into things that

are actually actionable problem solving

critical thinking let's go back now to

Jensen's statement nobody has to program

he says nobody has the program anymore

not nobody has to be an engineer a

scientist an

analyst but what to study now finally

going back to the rest of Jensen was

saying when asked about what people

should learn these days what would you

give me as an advice for something to

pursue if I were starting all over again

I would realize uh one thing one of of

the most complex fields of science is

the understanding of biology human

biology what he follows up with is also

very interesting nobody in computer

science nobody says car Discovery we

don't say computer Discovery we call it

engineering and every single year our

computer science our software becomes

better and better than the year before

however Life Sciences is sporadic life

science to life engineering is upon us

and that digital biology will be a field

of engineering not a field of science

Life Sciences to life engineering drug

Discovery to drug engineering as someone

who has a degree in both pharmacology

and computer science I can definitively

say that as it currently stands drug

Discovery life sciences and Engineering

are very different like seriously most

of the drugs we discover are actually

just wandering around the universe and

on a whim just discovering that a petri

dish of bacteria is being killed by some

sort of thingy that we now know as

penicillin and some dude wandering

around the Eastern Islands laying around

with dirt found this microorganism that

just happens to produce an antibiotic

which we now know as rapamycin I hope

that this is going to start a whole

generation of people who enjoy working

with proteins and chemicals and

Engineering these amazing things that

are more energy efficient all of these

inventions are going to be part of

engineering not scientific discovery I

hope so too that those of us who are

Engineers data scientist data analysts

technical people use our expertise to

engineer Solutions in the life science

Sciences drug Discovery climate change

thank you all so much for watching this

video please let me know what your

thoughts are about this topic and I'll

see you in the next video or live

stream