AI-Powered Software Development: Beyond Code Generation

Video ID: JybsMeYok2k

YouTube URL: https://www.youtube.com/watch?v=JybsMeYok2k

Added At: 13-06-25 21:18:53

Processed: No

Sentiment: Positive

Categories: Education, Tech

Tags: programming, AI-assisted development, ChatGPT, command-line app, project planning, requirements gathering, design solutions, user stories

Summary

The video demonstrates the use of AI development tools to create a command-line app. It uses ChatGPT to generate requirements, design solutions, project plans, and user stories. The video shows how to leverage AI throughout the project lifecycle to deliver projects faster and offload tedious work.

Transcript

in this video we'll be using AI
development tools to create this app
it's a command line tool that lets you
select a Target shell environment
Windows command prompt Powers shell or
Linux
bash you can ask for the exact command
to accomplish some task in that shell
for example how do I list the files in a
given directory in Reverse date order
the llm will give you the exact command
for that shell environment and then the
app will optionally allow you to just
copy that command into your buffer so
you can go and past and run it if you're
familiar with GitHub co-pilot this is
similar to its command line
utility the app will be cool and useful
and we'll certainly be doing coding in
this video but we'll do a lot more I
broken this video down into chapters as
usual and you're certainly free to skip
ahead to the sections that you find most
interesting however I highly recommend
that you watch this video in order while
we will build a working app my intention
here has more to do with getting you
thinking about how you can leverage AI
throughout your Project Life Cycle to
not only deliver your projects way
faster but also just to offload a bunch
of the tedious boring work that you
don't like doing in the first part we'll
be using chat GPT to generate our
requirements our design solution our
project plan and our user stories and
we'll get all of those into some nicely
formatted documents that we can just
download and place on our web server now
that first section is only about 5
minutes and I think you'll find it well
worth your time after that section we'll
get right into our IDE and start using
the ader AI coding assistant to build
out our app the really cool part is that
instead of Us coming up with all sorts
of complex prompts to feed AER to
generate our code we're going to have
AER directly fetch those web pages that
chat GPT generated for us and it will
use those to easily create the code for
our app let's get going we'll be using
chat GPT for the first few steps before
jumping over to our IDE for the real
fund the coding these steps are
necessary you'll see why as we go here's
the general description of a project we
have in mind we reviewed this earlier
I'm asking chat GPT to use it to craft a
better problem statement we do this for
two reasons first and foremost to
provide needed context for chat GPT and
later our other AI tools and two to get
some nicely formatted project
documentation out of this at the end so
using our basic project description chat
GPT generates a better crafted and
nicely formatted problem statement for
us as you can see this problem St is
much easier to understand and is more
complete than our original informal
description and this will remain in jat
gpt's memory to guide it and helping us
with the next tasks and our next task is
generating a set of project requirements
based on the problem statement here
we're asking chat GPT to use the problem
statement it just generated to create a
document that lays out the requirements
for our project we also give some
constraints such as this is an MVP so
keep it
simple and now we have a nicely
formatted set of requirements chat GPT
has covered the functional requirements
we provided and also offered some
non-functional requirements such as
performance and usability I looked these
over and they seem very reasonable to
me but I just thought of another feature
I'd like to add to the app this is an
app that runs in the user terminal to
help the user out and just to make it a
bit cooler the output should be
colorized with different aspects of the
output showing in different colors let's
make that update to our
requirements and here's our update
just thought of another feature i'
forgotten we want users to be able to
make multiple requests of our app so
we'll have chat GPT upd requirements to
make sure the app Loops processing user
request until the user opts to exit the
app and we can see that chat GPT updates
our requirements for looping now we
often have a chosen text stack in mind
maybe it's what our team knows maybe we
have another reason to prefer a certain
set of Technologies for our purposes
I've created a very simple short list of
Technology will prefer for this project
python the cloud 3.5 Sonet llm and
anthropics API library and I put this
info into this text
document now I'm going to drag this
document into our chat GPT session now
chat GPT is aware of our core technology
preferences we'll ask it to use the
problem statement and requirements which
it has in its memory already along with
our Target text stack to propose a
technical solution to our
requirements the proposed solution
document provides an over overview of
our Tex Tack and what the tool will do
then it shows the main proposed software
components and their
responsibilities then it walks through
the General application usage and flow
and finally it wraps up by summarizing
the benefits of the proposed solution
this is an MVP to be completed over a
4-we period so we can probably make do
with a very simple project plan chat gbt
already knows what we're trying to
accomplish as well as our high Lev
technical approach we'll give it a bit
more info on our team and let it know we
work in onee Sprints let's get chat GPT
to propose a project plan for us chat
GPT laid out a tenative project plan
based on our requirements technical
approach and the guidance we gave it
it's broken down task by team roll and a
timeline for the 4-we period And it
honored our request to have a start
coding by day three now we and chat GPT
have everything we need to start
creating user stories we're using Jura
so we'll have chat GPT start generating
Jura tickets events happen and plans
change so there's no need to plan in
detail past the first Sprint we can
always come back and ask for new user
stories later here we're asking chat GPT
to use all the knowledge of our project
it has in memory to generate all the
Jura tickets it thinks we should attack
in the initial
Sprint we see it generated a set of user
stories for Sprint one it formatted them
nicely for us and included titles
descriptions acceptance criteria a set
of tasks and even assigned a team member
role for each ticket we have tickets for
the developer for the product manager
and for the tester it's great that we
have all this information nicely
formatted in chat GPT but we need to get
this documentation into a knowledge base
of some sort we'll start getting all
these docs into web pages starting with
our problem statement we ask chat GPT to
put our problem statement into a
downloadable web page for
us chat GPT gives us a link we click to
download our problem statement web page
and there it is now we'll place this on
our web server later to make it
available next we'll do the same for our
requirements and project plan
documentation we'll end up with
downloadable web pages that capture
those as
well and here's our requirements web
page and now to generate the web page
for our project plan and here's our
project plan last step before we use all
this great stuff to start building our
out let let's get the juror tickets chat
GPT created for us into a web page for
now I'll just ask that the tickets
assigned to the developer role be
included in that
document and this is the result a
formatted web page with all the Sprint
one jur tickets for the developer
role just a note I'll be placing all the
web pages we downloaded from chat GPT
onto my local Dev machine and hosting
them using a very simple python web
server I start with this command okay
we're done with chat GPT for the rest of
this video from here out we'll be in our
ID e actually building and testing our
app I've launched vs code with an empty
project and I'm going to start the AER
AI coding assistant using the cloud 3.5
Sonet model it asks if we want to
initialize the project forget and we say
yes now the first thing we're going to
do is going to let it know what we're
going to provide to it to help it build
our project what we're going to do is
we're going to provide it the web pages
for the problem statement the
requirements the solution and the Jura
tickets we want to just let it know that
and say don't do anything just store
this knowledge in your memory it says
104 we're good to go gotcha next steps
so what we'll do next is we're going to
use a/ web command and provide the URL
to our problem statement hosted on our
local python web server so now ader is
going to download the contents of that
page and store the contents in its
working memory and it explains and
summarizes that to us to let us know
that it downloaded it and it
understands next we're going to provide
AER with the URL for our requirements
web page so it's also going to consume
that page it's going to add it to its
working memory it confirms that it's now
stored both the problem statement and
our requirements in memory and
summarizes our requirements very nicely
for us now we'll provide AER with the
web page for our design solution aer's
going to download that as well add it to
its context and then it's going to come
back and confirm now I have your problem
statement your requirements and your
design solution in my working memory and
ready to
go the last project document we'll
provide to ader is the one that contains
all our developer user stories for the
first
Sprint once it's downloaded this AER has
a complete picture of our project
including our requirements our technical
approach and the initial set of
functionality we want to build this will
save us from needing to explain all this
to AER so we can just get going with
building the app one thing that's often
helpful in guiding coding assistance is
up-to-date documentation on the
Technologies you're using this is
particularly true if you're using apis
and Frameworks that are recent or get
made major updates frequently one API
we're using is anthropics API for cloud
llm access here's the web page for that
API that walks through various usage
scenarios providing both code Snippets
and explanations we'll have AER add this
to its context as
well I let AER know that I'm going to
provide it a web page with some API
information to add to its
memory then I use the/ web command to
have ader download that
content now ader has up-to-date
information on using anthropic API the
CLA 3.5 signup model is fairly new at
this time so to make sure AER has what
it needs to Target the CLA 3.5 model I'm
going to have it also add this web page
I found that contains a short code
snippet that uses that API to access
Cloud 3.5 we tell AER about this new web
page we're going to have it add to its
context then we add that page to
AER now AER has the knowledge it should
need to start building our app we don't
need to explain the details of what ader
should do first it knows our
requirements our text stack and our user
stories so we'll just reference our
first user story and tell AER to execute
it as a reminder this is our first user
story which only involves bootstrapping
our project and installing our
dependencies notice here that I instruct
AER not to do anything beyond what
stated in that user story AER analyzes
that user story and begins generating a
solution for us it explains the solution
to us including the artifacts it plans
to create AER then gives us instructions
for the exact commands to run to install
our dependencies
then it asks for permission to create
the new files that contain the code it
generated we answer yes and AER creates
those artifacts and commits them to get
let's take a quick peek at the files
aer's created so far we have a very
simple main python script with minimal
functionality we also have a doget
ignore file to instruct get on the
project artifacts that should ignore and
a requirements.txt file that contains
the libraries and versions our app will
need ader gave us the commands we need
to run to install our
Tendencies let's go ahead and run those
now first we create a new python virtual
environment and then activate it then we
run the PIP install command on our
requirements.txt file to get our
dependencies
installed finally to confirm all is well
we'll run the main.py
script and it works as expected at this
point now I happen to notice that ader
missed a library dependency that was
specified in our user story it's not in
our requirements.txt file the story
stated we needed the piper clip Library
let's try getting ader to add this we
tell it to review the story again for
any Mis
dependencies AER did pick up on one
dependency will eventually need the
click library but it still missed Piper
clip let's give it another nudge we'll
be more specific this time all right
ader added our Piper clip library now to
the dependencies list to get our two new
libraries installed we'll need to
reexecute the PIP install command for
the requirements.txt file so now we're
good to go let's move on to our second
user story our jur ticket States the
user should be prompted for the Target
shell environment for which they'll
request command assistance the option
should be Windows command prom power
shell and Bash we'll tell AER to go
ahead and implement this user story
notice that we're only providing the ID
of the Jura ticket to AER and we're also
instructing it not to generate anything
beyond that ticket right now
AER generates the code changes necessary
to satisfy the ticket and then at the
end it summarizes the changes that's
made and looking at the main.py file we
can see that it's using the python click
library now to present three choices to
the user command Powershell and Bash
we'll go back to the terminal and run
our main.py file again to see the
changes we can see now we're being
prompted for command Powershell or bash
script and for a query so we're going to
say hey I want to find out how to list
files and it's echoing our query just as
our ticket says it
should so technically we satisfied the
ticket however I don't know about you
but I'd rather not have to type in bash
command and Powershell I'd like to have
a select list presented to me of my
options and entered a number so let's go
ahead and ask AER to fix that for us
make it a little bit more user
friendly and in reviewing the code
changes AER just made it does look like
we should should now be presented with a
numbered list of options for each of the
shells to choose from to test the
changes we just made work we'll go and
run the main.py file again and when we
run that script we'll see that we're now
presented with a numbered list of
options for command Powershell and Bash
we'll select option three for bash it's
echoed back to us you selected bash and
now we'll just make sure that the rest
of the functionality hasn't broken we'll
say we want to list files and our
queries echoed back to us
now our app isn't yet sending our
queries to the cloud llm as you can see
in this story CLI 6 adds integration
with the
llm so let's go ahead and have AER
implement this user story next we go
back to AER and just like with our
previous J of tickets we'll just give
the ticket ID to AER and ask it to add
only the functionality for that
ticket so ader now generates the code
for that ticket and then summarizes the
changes for us
and we can see in the code some of the
changes it implemented for integration
with the anthropic API in the cloud 3.5
model okay let's go test out our
integration with Claud we'll go back
over to the terminal we'll run our
main.py script now we select our shell
bash in this case we're going to enter a
query list the files in the current
directory and reverse date order and
oops we've got our first error looks
like ader made a mistake with the
anthropic API so to fix that we're going
to copy that error go back over to AER
we're going to give AER that error and
ask it to try to correct it now ader
makes some code updates so let's go back
again and test to see if it fixed the
bug we rerun the main.py script again
and give it the same query as we did
before oops another error this time I'm
guessing it's on us because it's
complaining it can't find an anthropic
API key let's go ahead and see if we can
get some more help from AER to fix this
one we'll copy that error
we'll go back over to AER terminal and
we'll paste that error in and we'll ask
for some help from
AER after it does some analysis it comes
back and says hey there's a EnV file
that you need to set an anthropic API
key environment variable in I'm going to
go ahead and update m. AMV with my API
key
offscreen now we'll retest our app we'll
provide the same user inputs as we did
last
time and this time we get a bash command
that satisfies our query this means that
our app is now integrated with the
Claude 3.5 model now let's copy the
command clog gave us and exit the app to
see whether the llm gave us the right
command for our query we paste the
command in our terminal and that output
looks correct we'll Implement one last
user story together we're done with the
developer stories for Sprint one so
we'll choose the first developer story
from Sprint two this story adds some
color to our so far boring app output
now our Sprint two stories are on a
different web page than our Sprint one
stories were we haven't yet had AER load
the Sprint two stories into our context
so first order of business is to have
ader load that web page into its context
we're going to start by informing AER
what we're about to do we're going to
give you the jro tickets for Sprint 2
then we'll use aers web command with the
URL for the web page to have AER add the
Sprint two ticket to its context and
just as with the other jur tickets we
ask ader to implement just that jur
ticket without any other functionality
and AER makes the changes for
us AER summarizes the code changes it
makes and it also tells us which colors
it's going to use for different parts of
the output now let's go over and test
our application
again and now we can see that our
output's colorized with different parts
of the output in different colors
depending on the role of that particular
part of the output let's have ader
generate a unit test for the select
shell function at the end it gives us a
summary of the unit tests it created and
some enhancements it's making to the
main code to make the code more testable
we'll go run these unit tests here's the
unit test gript that AER generated we
can run the tests and see they're
passing of course we've only executed a
portion of the Project's use cases in
this video we sto development after
adding color to the app's output but the
developer also has a Sprint two story
that allows the user to request the app
to copy the generated command to the
buffer and in Sprint three the developer
has other stories such as optimization
and later for enhanced error handling
etc etc and the tester and project
manager also have assigned work and even
though we won't be directly addressing
the use cases we've documented
throughout the rest of this video stick
around because there's several other
cool things I want to share with you
also in the description I'm going to
provide the link for the GitHub repo
where you can clone this project if you
want to run it yourself it's a more
complete project than the one that we've
developed so far for instance that
version of the project which you can
clone has the copy command to clipboard
functionality so here we can see we've
run our application and it's asking us
if we would like to copy it to the
clipboard we'll say yes and I'll simply
paste that into my terminal and you can
see it's the same command that the llm
generated here I'll run that command and
we're good so now that represents the
full functionality for our application
again you can download that from GitHub
repo now we can use AI to analyze our
existing code for issues and
improvements as well to demonstrate this
I've added a function to our main.py
file that's never called I'll ask AER to
review our codebase to locate dead
code and we can see AER finds that dead
code so let's have AER go ahead and
remove that for us
when AER finishes we can see that that
function no longer exists in our code
our project needs a readme file AER can
do a great job of creating one for us we
ask ader to create one that describes
what app does the Technologies it uses
and how to run and use the app ader uses
all the knowledge it has of our app to
create our readme and we can see that
did an outstanding job it's covered all
the bases re
requested app description Technologies
used how it works how to set up and run
the app app and how to run our unit
tests but we're not quite done yet one
last step we'll ask AER to generate a
sequence diagram that shows the main
flow between our apps components in
plant uml
format we tell AER to place the plant
uml in a file named sequence.
PML AER produces the plant uml and we
can now see a file named sequence. PML
in our project folder we can install a
vs code extension named plant uml that
will let us turn this text file into an
image with this extens ition installed
we can open the sequence. PML file then
right click and select the preview
current diagram option to view it as an
image we then select the copy button to
save this image as a PNG file to our
workspace and just like that we have a
really useful sequence diagram contained
right within our codebase that we can
embed into our external architecture
docs or into our readme we could also
generate other diagrams such as
component or class using the same
procedure I hope this video inspired you
to explore more ways to use AI to get
more done and have more fun there are
other videos on this channel that show
how to use AI to develop apps if you
found the topics in this video
interesting I think you'll find those
videos enjoyable as well if you'd like
to see more videos on this topic or
similar topics please do let me know in
the comments below and if you enjoyed
this video please like And subscribe I'm
Tim kitchens coding the future with you
and I look forward to seeing you in
future videos