Using AI in Software Design: How ChatGPT Can Help With Creating a Solution Architecture | R. Müller
Summary
TLDRRal Miller, an expert in software and architecture, discusses the utilization of AI in software design, specifically focusing on the capabilities of GPT models. He highlights the importance of context in AI interactions and shares practical tips for effectively using chatbots like GPT for complex tasks such as preparing for the iSAQB Advanced exam. Miller emphasizes the iterative process of refining prompts and the potential of AI as a collaborative tool in architectural decision-making, while also addressing considerations around data protection and the ethical use of AI.
Takeaways
- 🚀 Ral Miller is a renowned software and architecture expert, author, and accredited trainer known for initiating the open-source project doc toolchain.
- 📈 Ral discusses the effective use of AI, specifically chatbots like CPT, in software design and creating solution architectures.
- 💡 The importance of data protection and copyright is highlighted when using AI, emphasizing the need to handle personal and company information with care.
- 📚 Ral shares his experience and tips on using AI for preparing for the iSAQB Advanced exam, noting the differences between GPT-3 and GPT-4 models.
- 🌟 GPT-4 is recognized for its superior performance in a medical exam study, showcasing its ability to provide higher quality responses compared to GPT-3.
- 🧠 The core functionality of AI models like GPT-4 is likened to an all-knowing monkey rather than a statistical parrot, indicating a more complex and knowledgeable system.
- 🔍 Ral introduces the concept of 'embeddings' and their role in enhancing AI models, allowing them to access and utilize additional information beyond the core neural network.
- 🔗 The significance of context in AI interactions is discussed, with strategies provided for maintaining context and ensuring effective communication with AI.
- 📝 Tips for priming AI models are shared, including the use of custom instructions to establish long-term memory and refine the AI's responses.
- 🛠️ Ral demonstrates the practical application of AI in solving complex tasks, such as the iSAQB Advanced exam, by iteratively working with the AI to generate and refine solutions.
- 📈 The potential of AI as an architectural co-pilot is emphasized, with Ral noting that while AI can provide a good starting point, human guidance and review are still necessary.
Q & A
What is Ral Miller known for in the field of software and architecture?
-Ral Miller is a well-known software and architecture expert, author, and accredited trainer. He started the open-source project doc toolchain, which focuses on the effective documentation of software architecture. He is also responsible for creating various formats of the arc 42 template.
What is the main topic of Ral Miller's session?
-The main topic of Ral Miller's session is 'Using AI in software design', specifically discussing how chat can assist in creating a solution architecture.
What are the potential dangers of using CCK GPT mentioned in the transcript?
-The potential dangers of using CCK GPT include issues with data protection, both for personal data and company information, and copyright concerns when working with non-public files.
How does Ral Miller describe the core functionality of large language models like GPT?
-Ral Miller describes the core functionality of large language models like GPT as being based on a neural network that performs auto-completion, similar to how a mobile phone predicts text based on probabilities.
What is the difference in performance between GPT-3 and GPT-4 as discussed in the transcript?
-The transcript mentions a study where GPT-4 scored an 82% accuracy rate on a medical exam with roughly 1,000 questions, compared to GPT-3.5 which scored 65% on average and GPT-3 with 75%. This indicates that GPT-4 has a higher quality and more capabilities than GPT-3.
How does Ral Miller suggest maintaining context when interacting with chatbots like GPT?
-Ral Miller suggests starting every session with a prompt that includes a special character like a greater sign (>) to help identify whether the system is still within the context. As long as the context is active, the first paragraph of each output will include the dash. Once the context is lost, the system will no longer display the quote character.
What is the role of embeddings in enhancing the capabilities of a neural network like GPT?
-Embeddings are used to extend the neural network with additional data. They are created from text fragments and stored in a vector database. When interacting with the system, it may query this database for text fragments that fit the prompt, pulling them into the context and enhancing the system's understanding and responses.
How does Ral Miller prepare the chatbot for a specific task?
-Ral Miller prepares the chatbot by providing it with a detailed context, or 'priming', which includes information about his background, the goal of the session, and any specific requirements or constraints. He also uses custom instructions to guide the chatbot's responses and to create a 'long-term memory' for the chatbot.
What is the significance of the context size for GPT models?
-The context size determines how much of the chat history the model takes into account when generating responses. GPT-3 has a context size of 2,000 tokens, while GPT-4 has increased this to 128,000 tokens. A larger context size allows the model to consider more information, potentially leading to more accurate and relevant responses.
How does Ral Miller use chat GPT to assist with an advanced certification exam?
-Ral Miller uses chat GPT to generate solutions for the tasks in the exam by providing detailed prompts and context. He also uses it to understand the task better by asking the chatbot questions and refining the prompts based on the responses. The chatbot helps in creating documents, diagrams, and strategies as part of the exam.
What are Ral Miller's views on the future capabilities of chatbots like GPT?
-Ral Miller believes that chatbots like GPT can serve as an architectural body co-pilot, helping to generate ideas and make decisions. He expects that advancements in the model will lead to an even larger context size, which will help to overcome current limitations. He also sees potential in using GPT for passing oral exams by integrating it with virtual avatars and video technology.
Outlines
🤖 Introduction to AI in Software Design
Ral Miller, a renowned software and architecture expert, discusses the use of AI in software design. He highlights the importance of considering data protection and copyright when using AI tools like GPT. Ral introduces the concept of chatbots in creating solution architectures and shares his experience with GPT, emphasizing that while GPT is often seen as a stochastic parrot, he views it as an all-knowing monkey due to its extensive data training and ability to provide detailed answers.
🧠 Understanding the AI Model's Architecture
The speaker delves into the architecture of AI models, comparing GPT-3 and GPT-4, and noting the differences in their training data and parameters. He explains that GPT-4 has a more complex structure and can process multimodal inputs, including images and voice. The discussion includes the concept of 'embeddings' and how they extend the AI's knowledge base beyond its training data, suggesting that the AI model is more than just a neural network.
📝 The Role of Context in AI Interaction
The importance of context when interacting with AI is emphasized, with the speaker explaining how the AI model uses context to generate accurate responses. He provides tips on maintaining context during a chat session and how the model's context window can affect the quality of its answers. The speaker also discusses the concept of priming the AI with information to ensure it understands the discussion's goals and background.
🎯 Preparing for the iSAQB Advanced Certification
The speaker shares his approach to preparing for the iSAQB Advanced Certification using AI. He outlines the process of priming the AI with personal and professional context, discussing his preferred programming languages and architectural styles. The speaker demonstrates how to use the AI to understand and work with exam materials, including rules and glossaries, to build a solid foundation for tackling the certification exam.
📚 AI's Ability to Interpret and Generate Content
The speaker explores AI's capability to interpret and generate content, such as diagrams and tables, from provided documents. He discusses the limitations of AI in reading images within PDFs and shares his experience in guiding the AI to produce the desired outputs. The speaker also highlights the AI's ability to ask relevant questions about a task, showing its active engagement in understanding and solving problems.
🛠️ Iterative Approach to AI-assisted Task Solving
The speaker describes an iterative approach to solving tasks with AI assistance, where the AI generates parts of the response to refine the solution. He discusses the process of working with limited output space and the need to edit prompts to keep the context concise. The speaker provides examples of how he refined his prompts to achieve better results, such as creating a utility tree and assessing quality scenarios.
🏗️ Building a Solution Architecture with AI
The speaker details the process of building a solution architecture with AI, discussing the creation of a context diagram, business models, and a technology stack. He shares his experiences in refining prompts to improve the AI's outputs and emphasizes the importance of checking and iterating on the AI's results. The speaker also discusses the potential of AI to serve as an architectural co-pilot, providing valuable insights and options for problem-solving.
🚀 The Future of AI in Architectural Decision-Making
In the concluding remarks, the speaker reflects on the potential of AI in architectural decision-making. He discusses the advancements in AI models and their ability to transform input into solution outputs. The speaker acknowledges that while AI has limitations and requires guidance, it serves as a valuable tool for generating ideas, making decisions, and identifying errors and inconsistencies in architectural designs.
💬 Q&A Session with Ral Miller
The Q&A session with Ral Miller covers topics such as the time and effort required to solve problems with AI, the creation of embeddings, and the AI's ability to surprise with new ideas. Ral discusses the ethical considerations of using AI in exams and his experiences with using AI for open-source software inquiries. He also addresses concerns about the use of IP in training sets and the importance of using AI responsibly.
Mindmap
Keywords
💡AI in software design
💡Chatbot
💡Solution architecture
💡GPT-3 and GPT-4
💡Context
💡Embeddings
💡Prompt engineering
💡Data protection
💡Advanced software architecture certification
💡Multimodal capabilities
Highlights
Ral Miller is a well-known expert in software and architecture, and the creator of the open-source project doc toolchain.
Ral Miller emphasizes the importance of considering data protection and copyright when using AI in software design.
GPT-4 has been trained on terabytes of text data, resulting in more parameters for providing better answers compared to GPT-3.
GPT-4 scores higher on tests, with an 82% accuracy compared to GPT-3's 65% and human average of 75%.
Ral Miller refers to GPT-4 as an 'all-knowing monkey' rather than a 'statistical parrot' due to its extensive knowledge base and improved capabilities.
GPT-4 has a context size of 128,000 tokens, a significant increase from GPT-3's 32,000 tokens, allowing for a larger window of context.
Ral Miller shares his experience using GPT-4 to prepare for the iSAQB Advanced exam, demonstrating its potential as a study tool.
The speaker discusses the use of embeddings to extend the neural network with additional data, which is stored in a vector database.
Ral Miller provides tips on maintaining context during chat sessions with GPT-4 by starting each session with a specific prompt.
The importance of priming the system with relevant context, such as profession and preferred technologies, is emphasized for effective communication with GPT-4.
Ral Miller demonstrates the ability of GPT-4 to understand and work with complex diagrams and images from PDFs.
The speaker highlights the iterative process of refining prompts and checking the model's understanding to achieve high-quality outputs.
Ral Miller shares his success in using GPT-4 to generate a solution strategy for a software architecture task, showcasing its problem-solving capabilities.
The speaker discusses the potential of GPT-4 as an architectural body co-pilot, aiding in decision-making and providing different perspectives on architecture.
Ral Miller addresses the issue of bias in AI systems and shares his experience with how the system's generated image reflected a bias.
The speaker emphasizes the importance of checking and refining the results produced by GPT-4, as it serves as a starting point rather than a final solution.
Ral Miller concludes that GPT-4, with its advancements, is a valuable tool for transforming input into solution outputs, though it requires guidance and analysis.
Transcripts
Ral Miller is a very well-known software
architecture expert author and iqb
accredited trainer Ral started the
open-source project doc toolchain which
deals with the effective
documentation of software
architecture uh he's also responsible
for creating the various formats of the
arc 42
template yeah and enjoy now his session
on using AI in software design
thank you MCO for the uh introduction so
I can skip my first slide hello and
welcome to my talk using Ai and software
design how chat can help with creating a
solution architecture let's skip the
slide because you know already know
everything about me and um time is
precious today because I have lots of
slides but first up a
disclaimer be aware of the dangers when
you use cck GPT keep data protection in
mind both in regards of personal data
and company
information copyright might be an issue
when you work with um with CPT if we
drop files which are not public
available and we will I will show you
how to solve the um isq aqb Advanced
exam with chpt but don't do this with a
real exam you not allowed to do
so
now another
disclaimer I am not an AI expert I am
only a prompt engineer I'm a user of
chat GPT and um I think I I gathered
quite some experience by now so I will
show you today my tips and tricks
most of the time when um people talk
about CPT and other large language
medals models they say it's a stochastic
parrot because there's a neuron Network
at the core of the system and it just
yeah does some kind of autoc completion
like when you type in text on your
mobile phone based on
probabilities that's the core of the
large language
model but I believe it is more the all
knowing monkey and why I think so I will
now first try to tell you and introduce
some Basics about
chpt so if you compare the freely aaable
CET
gpt3 with the not free aaable CG 4 you
will notice that there's a huge
difference um cpt4 has been trained on
lots of data terabytes of Text data by
the way most of the information I
present here I Googled in the inter on
the internet but um there's not much
reliable data out there but it gives you
some yeah rough estimates
so much more data in the training and
many more
parameters which are needed to give good
answers so gpt3 is available for free so
many people who talk about CET GPT and
who have checked it out are talking
about the free
model because you have to pay 20 bucks
per month to use the GPT 4 model at
least at least if you use a um chat GPT
4 by open
Ai and if
you search deeper for uh comparisons
here's for instance a study about a
medical exam with roughly 1,000
questions and here you see the
difference between those two models GPT
3.5 scores uh 65% of the test humans on
average
75% GPT 4
82% so it has really a different
quality the GPT 4 model than the gpt3
model and it is more than just the
neural
network but it's hard to find details
about it um it's based on the
Transformer model architecture and you
find this picture on the internet and
yes we are Architects and we should be
able to read this uh this diagram but um
for me it's not so yeah doesn't give me
so much meaning so much background as I
expected so for this talk I came up with
my own diagram and it's um yeah
partly guesses it's quite abstract but
you will notice what I want to tell you
at the core there's a neuron
Network and you input text to the model
and before this text hits the neural
network we already have a natural
language processor so it's not only the
neural network at the core but there's
something in front of it
and then when we output data there's an
output
processor and then we get our text
output and um there's also the system
also feeds context to the NLP
processor we will talk in a minute about
the
context and it seems to be not only one
neural network in at core but a mixture
of
experts so in the past when um you asked
a question how to mathematical question
what's 1 +
one it went through the normal neuronal
Network and it had some problems with
bigger numbers or more complex
calculations now at the core seems to be
this mixture of experts so something
decides uh to which expert model the
question should be
rooted and so it gained much more
knowledge and can um lead to better
answers but it also now is a
multimodal um yeah tool because it not
only works on text but you can now also
drop images which are then processed and
fed to the NLP
processor and even um you can ask it to
Output images through Deli
3 and voice you can speak to
it and it can generate speech so you can
um not only type in your questions but
you can just um work with it with your
voice there's a Code
interpretor uh which is capable of um
yeah doing using Python scripts to work
on your
prompt and there are many more plugins
aailable which makes the model quite
powerful so you see it's not only just a
neon Network at a core it's many more of
it and there was one article where they
stated that jet gptt 4 has around
110 layers whatever this means I think
it's not the layer of the neural network
it's the
layers before and after
it but as I said I'm not the AI expert
I'm the prompt
engineer so let's talk about context
when you chat with the
system you type in your text and first
should be some kind of priming you
should give the system some context who
you are what you know and what you're
are going to do in this example hey
let's work on a web- based application
with spring Boot and
MySQL and then you chat with a system
and
everything which is here in the chat in
the history will be part of the context
of the system will be fed to the
neuronal network this is quite important
because if you continue your
chat there's a window of
context and
now some messages has left this context
will not be fed anymore to the
model and this means that your first
statements the priming leaves the
context and the system doesn't know
anymore what we are talking about and it
will start to give wrong
answers so how important is this
fact chck gpt3 has a context of 2,000
tokens GPT 4 or has a context yeah in
the past of uh 32,000
tokens since roughly four weeks um they
increased managed to increase the
context to
128,000 tokens a token is not a word
it's a part of a word so those um 32,000
tokens are roughly 20,000 worth
so it takes some time to get out of this
context but it depends on the model you
work with and for each model there are
different models with different context
sizes so the context size of
128 uh K is only aailable through the
API at the
moment so here's my first tip to see
whether you are still in cont text or
not just start every session with this
prompt which tells the system to start
every response with a greater
sign because it
outputs um it's text with markdown
formatted the greater sign will be
outputed as a
quote which will look like this so the
first paragraph of each output
will be this
Dash as long as we are in
context the first time we leave the
context it will forget about this prompt
and will also not display this uh quote
anymore but I wanted to tell you
that the system is not just a
probability machine not Sy stastical
parot and for this embeddings are quite
important um you want to extend the
network the neural network with your own
data but that's not possible because
it's already fully trained and you can't
easily train it with additional data the
solution for this is to use
text
and create vectors from these um text
fragments um there are some algorithms
which um create those vectors um which
just check out the words and see how
yeah how near they are to each
other quite complex I don't want to get
get into detail here but you have to
know about those embeddings that they
are different than what the model knows
um within the neuron Network and it's
also not yeah how it how it belongs to
the context because those text fragments
those vectors are stored in a vector
database and when you talk to the system
it always for each prompt might query
the vector database check out out
whether there is a text fragment which
fits your prompt and pull it into your
context so if it does this your context
will be smaller than what you chat with
a system so that's why it's important to
know about it and even if you have a big
context it's always a question how much
context you you really
have again GPT 4 32,000
tokens and we will see when we are in
the chat model the the chat
GPT we will have less we will have
something about 4,000 or 8,000
tokens so going back to the all knowing
monkey or the stastical parrot I I
believe with this approach those
multiple layers multimodal approach and
those
embeddings um the system behaves like
Yeah The all- Knowing monkey which has
access to all the knowledge of the world
at least the knowledge with which is
aailable to
it but let's get started with the real
details with the
yeah with trying to get a solution for
the advanced certification of the is
aqb so as I already said we have to
prepare the model we have to do some
priming every session every chat session
starts from scratch the model doesn't
know what you chatted with it before in
another session so you have to tell it
the context of your discussion it
doesn't know you you have to tell it
what it should know about you you have
to tell it what's the goal of the
session and maybe also what the solution
should look
like so for instance my context is that
I'm an experienced software architect in
the field of web development my favorite
programming language is Java or groovy
JavaScript and Cor responding Frameworks
are not my thing I prepare to get by
with minimal JavaScript okay that's me
interesting I um want to create an image
for this and here you see some kind of
bias and it's so funny that not even I
am depicted as as an old man it's also
that there really old um technology
calculator on the table so I gave it a
try and um see what kind of bias it uses
when I tell it U my favorite programming
language is Python and JavaScript that's
cool and I love modern JavaScript
Frameworks hey now I'm young and cool
and
tip so bias is a problem even where you
don't expect
it but let's move on with the
priming
um I shouldn't not only tell the system
yeah this this small paragraph I also
extended it to tell it more about um how
I work which Frameworks I I like to use
by the way there's lots of text on those
slides you don't have to read it um you
will get the slides
later so this is really enough context
uh for the model to understand how I
think how I work um what's my profession
is
um it isn't long ago that you had to
copy and paste such a priming every time
into your chat session which was not fun
at all but um now catp uh has custom
instructions and you have two text
Fields where you can put in um what
would you like chpt to know about you to
provide better responses so that is the
text from the last SL side and I call
this long-term memory because I can put
in there something which CHP should
memorize and the second part of the
custom instruction is how would you like
chpt to respond and there I also have
lots of prompts uh for instance start
every response with a greater sign um we
already had this but also many things
for instance um
be excellent at reasoning don't mention
your knowledge cut off um I already know
this and um that really
helps and um I also instructed that I
want to create ASD do output for my
documents and even how I want to have my
ask do output
formatted okay even more priming
I want to have the best basis for my
chat session with cck
GPT so I tell it today I want to talk
with you about a software architecture
certification do you know the is
aqb every time I start a session and um
talk with chat chpt I ask the model
whether it knows about the
Technologies um or other abstract things
I want to talk about it um so it first
answers yes it knows it then it
describes the is aqb and this tells me
that it yeah knows what it's talking
about and it also creates some
context for instance here I ask about
the advanced level
certification and it repeat the key
aspects of the certification adding this
to the context and helping both chpt and
me um for this
session by the way I think the answer
was much longer but um that isn't too
important for this
session now we are still at the priming
mode um so there are some um public
aaable PDFs for the exam and I um just
dropped in here the official
rules the model should know about the
rules I also dropped the glossery
because I'm not a native speaker of the
English language and um when we talk
chat GPT and me about um the the exam we
should use the same wording
and so I think it's important that it
knows about this
document and as you can see um those
documents are now put into the system as
embeddings and that should be enough for
the priming at a moment now we can start
with the real task we have a third PDF
document the example task big spender
for a um Advanced
certification I asked chpt to read the
document carefully but not start to
solve the task yet why every time you
drop something into chat GPT it tries to
come up with a good answer and here it
would try to yeah already um solve the
certification task but this would
clutter up the the context
so even with this it just tells me
something about uh the document that it
understood it and
um ask me if I have any
questions feel free to ask
great the PDF contains two images and I
already experienced that somehow images
in PDFs are hard to read for C
GPT but if I just drop them into the
chat and tell him hey here are my images
from the
PDF it really manages to read and
understand those
diagrams it can read the text in the
boxes it uh will notice the connections
and uh output what it sees there which
is quite
helpful 6 months ago when I submitted
the idea for the talk um jgpt wasn't
capable of doing all of this and I
wasn't sure whether I can create a
solution uh with chpt for it um it
couldn't read those images but what was
quite interesting there was a
description underneath um this class
diagram in in form of a table and I did
it the other way around I put in the
description and asked CHP to create a
diagram and the diagram didn't look like
this one and I was looking for the
reason and found out that the
description didn't fit the diagram in
all parts which is also quite
interesting one last
instruction I learned that CH gbt is
capable of asking me questions
so before you solve a task check if you
have any questions regarding the task
and ask me those
questions I pressed return and oops Yeah
it already had some questions it came up
with a list of 10 questions about the
task and I think quite good questions
like regulatory
requirements it does doesn't know that
I'm from Germany and U maybe uh one
regulatory requirement is a um data
Protection Law and other German laws so
this was quite
interesting and I answered all those
questions and you can answer them one by
one and every time you submit one it
will tell you okay now I understand but
please answer also the the other
questions so you can turn side
but the First subtask Quality
attributes I always let CH gbt repeat
the objective for the
subtask and um it repeats it adds to the
context and I see that it really knows
about the subtask
great so this worked this is the subtask
the objective now we can go on derve the
quality scenarios trade me an asky do
document okay and wow I got a table
quite nice I scanned it and looks
reasonable the importance we could work
on this it shouldn't be everything high
but quite a good
sample now we also want to have a
utility tree I asked CH to create one in
plant oml and as you can see here this
says three of
three so when I work with those prompts
I refine them and it doesn't make sense
because of the limited context to refine
The Prompt in the next prompt it makes
sense to edit your prompt
to keep the context short and here you
can see um I had three tries and uh
first I I just had the idea to Output it
as a plant uml mind map but then I
noticed it didn't came up with the um
IDs of the quality scenarios so I
refined this
and wow it tells me it created a file
which can be downloaded I wasn't aware
well that it can do so but it worked it
was not a hallucination so I downloaded
this file displayed it in plant uml and
here you go we have a utility
tree we have the quality attributes
everything's
fine now you can
see um yeah that's that's a third step
for this
subtask um again a table and um it's a
special table because I again refined my
prompt because first it had some
problems um with the motivation and I
then asked uh the system to to put the
motivation below those lines with a call
span of three and it knows how to work
with asid do tables and uh came up with
a good result
the result is a little bit questionable
I would have uh post other weights on it
so system reliable and availability only
10% yeah okay I think it should be more
but these are all things you can discuss
with a model and um so change its
mind so as you have seen I I let the
model create parts of the uh
response um just to to work on the
details because it has a limited output
space and uh it will not output the the
whole um solution for this one task if
you do not um work iteration by
iteration on it and so I then ask it to
uh give a solution for the whole subtask
and here we are it repeats the other
things it already created and looks good
to
me solution strategy that was an easy
one um
so but I had a problem with repeating
the objective um you can see I refined
The Prompt as stated in the document was
important because if I do not say as
stated in the document it just uh tries
to get it from the cont context if I say
as stated in the document it starts to
read the document again and outputs what
it finds in a document and this seems to
be quite okay it's it's a word nearly
word by word um repetion of the the
objective from the document so with this
I just um told it hey give me a solution
strategy
and um ah yeah and again I I told it if
you have any questions about this task
ask them
now and again I was surprised it came up
with some
questions and um it doesn't work as
expected that when I at the start tell
it whenever you have question ask a
questions you always have to repeat this
um the thing but it came up with those
questions I answered those questions
told it to um give me a solution
strategy and it came up as expected uh
with a key constraints assumptions and
basic
principles talking about the chat
context I can ask it how much of the
context is left and it tells me 2,478
tokens of 4,000 aable tokens so we are
still in context
great I repeat it and it tells me 2,300
tokens okay that's an
estimate of 8,000 tokens so it doesn't
know whether it has 4,000 or 8,000
tokens but we are still in context I
believe because what happened here we
don't have the quote anymore here in
front of it but that is just uh because
it sometimes forgets to Output it here
it is
again again uh objective for the third
statement for third subtask this time it
forgot something overview of purchased
and open source
Parts okay so in my
prompts
I yeah in the next prompt I I tell it to
that it forgot it but first I wanted to
to have the context diagram I thought
this is an easy task I told it to create
it as plant TL in the C4 notation but
what I got was too simple far too simple
it didn't remember what it needed for
this context diagram so this is
worthless
give the model time to think is an a
prompting approach where you yeah let
the model first generate the information
it needs so first name all actors who
will interact with the system name all
external systems and create a table with
uh the external
interfaces and now we get an answer and
with this answer we just repeat our time
ask our prompt from
um just two prompts ago and the result
is quite better and now I'm I'm happy
with um the actors and the external
systems let's go on with business
models we need a business
structure and um chat GPT can generate
it again we have a plant ml diagram uh
as
download and again I'm not happy with
this
diagram and again I do did some prompt
engineering and toed the system to
display all external components from the
subtask three in the diagram actors and
external systems and that changed the
display the the generated
diagram
the fifth subtask wants to have a
technology
stack again let's repeat the
objective and hey that is not the whole
OB objective because if you take a look
at um at the document subtask five is a
long
subtask and the problem seems to be that
it goes over a page break
this is a problem for
chip and in this case we I fix it by
putting all the text in the
context when working with uh those
embeddings it just fetches for each
answer those embeddings it needs now I
put it in the context and grow my
context and with this full
context um
I can
get a technology stack I'm happy with
this and uh it was also requested to um
explain the technology stack with the
workflow through the system and yeah it
tells me the system parts involved for
each step and the contribution to the
Quality goals looks fine for
me let's check the context again
tells me we used up 4,600 tokens out of
8,000 tokens
okay let's go
forward the final subtask
evaluation and in this case I extended
my prompt to repeat the full
objective so you have to be quite
detailed and concise with your prompt
and then I got um the the full objective
fine and this time I asked whether it
understands the objective and whether it
has questions regarding it and I was
surprised it says yes I understand the
objective and I don't have any specific
questions okay so go ahead identify the
top five riskiest and most important
quality scenarios here they are
fine and then I asked to create the
whole
document and as you can see data in inte
Integrity it has a rational essential
for legal compliance great architectural
decision implementing strict Access
Control good tradeoffs this might may
slightly increase system complexity but
it's
necessary I think those results are
quite good and now we finished the
exam and the question is how much
context is now left and now it tells me
we used up 5,000 tokens out of 4,000
tokens and the earliest parts of the
conversation are no longer in the
immediate
context we just used up all the the
context so I guess when you do it in a
real
scenario you will start the context from
fresh for each
subtask and
um I I guess it's also just a matter of
time that we get a chat GPT with a
really huge context and then these
problems will be
gone
conclusion CH GPT is a generative
pre-tin Transformer that's what GPT is
about and it is capable of transforming
the advanced exam input to a solution
output I think that's quite great as I
said six months ago I think that
wouldn't be such a good solution but now
with the advancements in the model I
think the solution is quite okay um it
has some problems but it is a good
starting point it's it's a architectural
body co-pilot for me and I now can work
from this Solution on and make it
better it needs Guidance the results
have to be checked
yes it's quite helpful can walk alone
that's what I mean it's a buddy um to
help me to get good ideas to um make
good decisions and to um for for
instance um get different views on uh on
the
architecture interly interesting if you
ask CHT to analyze the
results and ask it to find errors and
inconsistencies it will find the
shortcomings by its own so this is also
quite helpful to just ask the
system whether the things it produced
are good or
not now the advanced exam um consists of
two parts the homework which we just did
with
chup and an oral exam
where you have to show up and um answer
some questions and will jgpt be able to
pass the oral exam the technology for
this is already there you can create
virtual avatars you can speak to the
system and you can even uh fake video so
the technology is there but I think that
the it will be yeah you will notice that
the the solution was not created by a h
a human
architect and so you still have to
analyze the
solution understand it and be prepared
for the oral
exam to say it with the words of William
Gibson the future is already here it's
just not very evenly distributed so when
you think of gpt3 the free model and GPT
4 where you have to pay 20 bucks that's
not evenly distributed not everybody has
access to the better
models but it's already a very
interesting um
technology so thank you for your
attention the slides are aailable behind
this QR code there's also a discussion
board there and um the full transcript
and the solution as
PDF thank you for your attention I think
we still have three minutes left for
questions thank you Ral this was an
amazing talk and about a very important
topic um I have two questions for you um
the first would
be how long did it take you to solve the
problem how many attempts were
needed H it was quite fast it um I would
say two days to work with the system and
to um rework some of the
prompts but I also have to say that I I
only did a quick view over the results
they
look from a first view quite good but um
you will find uh problem s with
it okay thank you um the another
question um are there other means of
creating embeddings other than priming
interactivity uh by dropping documents
in the CET
GPT so when you just work with CET GPT
um I think this is a um main way to do
embeddings you can also by now um State
some URL which it then goes and fetches
them and um if you create your own
systems through the API uh you can use
the API to create embeddings to um for
instance use a larger um set of PDFs
create your own Vector database and thus
create those embeddings you then have
have the full control of it and um
because you always um you yeah you
create the chat you put in the the
context to the API and you decide how
much of the context you will fill with
the
embeddings um thank you uh there here's
another question um did Jet Chet GPT
really help you or did you try to make
Chet GPT understand insights you already
had
beforehand did it Sur surpris you with
good ideas you did not have
before yes it it's always surprises me
with good ideas and uh always helps me
um for instance it was closer to the
objectives of the task and I was and um
that was something where I thought yes
it's it's right these these were the
expected results I would have written
much more not just to to um tell how I
came to those uh
results but that's one thing I was
surprised of another thing um that it
asked me questions about the task and
the if you take a look at those
questions they make
sense and this also helps me to get a
better understanding of the task and the
next thing is when you work on
architectural decision records with cat
GPT um you can ask it for several
options how to solve the problem and it
might find options you didn't think of
so yes it helps it helps to extend your
your
mind okay and uh now a last question
because uh the time is running um for
the Break um what is your view on the IP
used in the training sets and in whether
your input or questions were then added
as training for the
model so most of the time when I work
with CET GPT um I uh asked very general
questions about um open source software
so that that's something I do not
consider as a problem when you work um
with a exam I could only work with a um
public aaable um example you're not
allowed to do this with a real exam and
also um I wouldn't put in um some
internal documents or something like
this or personal data um but the
interesting point is that um all those
companies who provide those large
language models um if you buy the
Enterprise Edition they promise you that
they don't use the data you put into the
chat for trainings or something like
this so I think there will be
yeah a switch in how you think about
this problem and how it will be
solved okay thank you uh there is one
thing uh um someone is asking for the QR
code uh maybe you can bring it up again
the QR code but um
I think it was in in your um
presentation you you can download the
presentations to the attendees you can
download all presentations uh uh later
uh we will provide it for you yeah so uh
I think or maybe I think I will also put
the link in in the chat okay this is
great okay thank you
Voir Plus de Vidéos Connexes
![](https://i.ytimg.com/vi/Fv7hpwUgXz8/hq720.jpg?sqp=-oaymwEmCIAKENAF8quKqQMa8AEB-AH-CYAC0AWKAgwIABABGE0gZChlMA8=&rs=AOn4CLCqQqTcHJE8qRgLUfVwXisJHBr2DA)
Using AI in Software Design: How ChatGPT Can Help With Creating a Solution Arch. by Ralf D. Müller
![](https://i.ytimg.com/vi/1UXyhj5iPBU/hq720.jpg)
Prompt Engineering - Corso professionale Parte 1/2
![](https://i.ytimg.com/vi/ktKB08E7vvw/hq720.jpg)
9 Ways To Use ChatGPT To Write A Literature Review (WITHOUT Plagiarism)
![](https://i.ytimg.com/vi/H9Hz6mmn5vY/hq720.jpg)
Basi e Principi utili del Prompt Engineering, l'arte di saper parlare con le AI Generative
![](https://i.ytimg.com/vi/1fwsbNwU_ts/hq720.jpg?v=65fb45cc)
AI Generativa
![](https://i.ytimg.com/vi/uvViegywkfY/hq720.jpg)
Barbara Gallavotti | Che cosa pensa l'Intelligenza artificiale
5.0 / 5 (0 votes)