LLM Starter Pack: A Pragmatic Guide to Success with the Large Language Models
Summary
TLDRAmir provides a pragmatic view on using large language models. He explains common applications like writing assistance and coding, but cautions about risks like hallucination. He advises experimenting to see if models solve your problem before deployment, considering costs and flexibility. Composition and design are key - build a stack with language models as one component. Sophisticated combinations of models can provide competitive advantage. Use powerful tools like language models, but with awareness of limitations.
Takeaways
- 😊 LLMs are very useful for writing assistance, coding, querying data, etc but still imperfect
- 😵💫 Beware of hallucinations - LLMs can make up convincing but false information
- 😏 Evaluate if LLM solves your problem before productionizing; consider inference cost
- 🤔 Minimize hallucination risks; add guardrails like human review, fact checking etc
- 🤯 LLMs enable cool things like coding co-pilots, talking to data, writing books etc
- 😎 Experiment with public models & tools to determine if LLM meets your needs
- 🔍 For production use, optimize model latency, cost etc with MLOps, quantization etc
- ⚖️ Consider open vs closed source models based on needs like privacy, cost
- 📏 Bigger LLMs learn faster but benchmarks may not reflect production readiness
- 🛠 Be composable - use LLM as part of a stack, combine with other models
Q & A
What are some of the main applications and use cases presented for large language models?
-Some of the main applications mentioned are using them as writing aids, coding co-pilots to help generate code, enabling natural language interaction with data, and using them to extract information from unstructured data sources like PDFs, videos, and audio.
What risks or downsides are discussed regarding large language models?
-The main risks discussed are the tendency to hallucinate or fabricate factual information, leading to incorrect or misleading outputs. Several examples are provided of language models generating convincing but false information.
How can the risks of hallucination from large language models be mitigated?
-Some ways to mitigate hallucination risks include minimizing exposure through careful prompting and design, putting guard rails in place with human oversight or fact checking components, and using large language models as just one composable component in a larger AI stack.
What considerations are mentioned regarding deployment of large language models?
-Key deployment considerations cover factors like cost, latency, privacy, and flexibility needs. Additional model optimization, quantization, and hardware-software co-design can help maximize efficiency of deployed models.
When is training your own large language model recommended vs leveraging existing models?
-Training your own model requires extensive data, compute budget, and specialized teams. In most cases, leveraging existing models with techniques like prompting and in-context learning can meet needs without costly training.
How can combining large language models with other AI capabilities lead to more advanced solutions?
-Using large language models alongside other AI modules like specialized NER or NLP models, knowledge graphs, etc. allows creating sophisticated solutions that accentuate different strengths.
What framework is proposed for evaluating if and how to apply large language models to a problem?
-The suggested framework analyzes whether large language models can actually solve the problem, if solutions could be deployed to production, flexibility needs, and risks like hallucination before deciding on best approach.
How crucial is model and prompt engineering highlighted in effectively applying large language models?
-Effective prompting and model optimization techniques are emphasized as critical to maximize large language model potential while minimizing cost and latency tradeoffs.
What is the outlook given on the future potential and current maturity of large language model technology?
-The technology shows great promise but is positioned as still maturing rapidly, requiring thoughtful application design and awareness of limitations in present form.
Why is a composable AI stack incorporating diverse technologies suggested over reliance on large language models alone?
-Combining large language models with other specialized AI components allows accentuating different strengths to create more advanced and resilient solutions.
Outlines
😀 Introducing the speaker and topic of large language models
The paragraph introduces the speaker Amir and the topic of large language models (LLMs). It talks about familiar faces in the audience, excitement to share information on LLMs, whether they are overhyped, and how they can be used to build something useful. The talk will take a pragmatic approach.
😟 LLMs have imperfections like hallucination risk
The paragraph discusses that LLMs have risks like hallucination where they make up convincing but factually incorrect text. Research involves dealing with uncertainty and imperfection. The pragmatic approach is building something useful with LLMs while managing the risks.
😲 Examples of LLM failures and hallucination
The paragraph provides examples of public LLM failures like Meta's Galactica and Google's Bard, and lawyer's use of chatGPT. This highlights the risk of hallucination where LLMs make up convincing but false information.
🤔 Framework for deciding if and how to use LLMs
The paragraph introduces a framework for deciding if and how to use LLMs. Questions include - does the LLM solve my problem based on metrics, can it go to production, how flexible on cost and latency, and how bad would hallucinations be.
🔍 Tradeoffs between LLM size, accuracy, cost and speed
The paragraph discusses tradeoffs between LLM size, accuracy, computational cost and speed. Bigger LLMs have better few-shot learning but smaller ones can be more practical. Aim for simplicity with the smallest LLM that solves the problem.
🚀 Optimizing LLM deployment cost and latency
The paragraph covers optimizing cost and latency when deploying LLMs in production. This includes model quantization, pruning, server optimization using chips tailored for inference.
😎 LLMs now possible even on laptops
The paragraph shows exciting possibility of running LLMs even on laptops today. This enables easier prototyping and experimentation.
❔ Alternatives before deciding to train your LLM
The paragraph discusses trying in-context learning and prompting fine-tuned smaller models before deciding to train your own expensive LLM which needs massive data and compute.
Mindmap
Keywords
💡large language models
💡hallucination / fabulation
💡in context learning
💡inference cost
💡composability
💡hardware optimizations
💡open versus closed source
💡design considerations
💡productization
💡limits of utility
Highlights
Large language models can hallucinate and make up convincing but factually incorrect information
These models are useful as writing aids, coding assistants, and for talking to data
Put guardrails in place when using these models to minimize risk of failures from hallucinations
Use simple solutions like prompting and smaller foundation models where possible rather than large complex models
Test models yourself on your specific use case rather than relying only on benchmarks
Bigger models can solve problems with less data but still may not reach necessary accuracy for real applications
Quantizing and pruning can optimize large language models for efficient deployment
In-context learning and prompting can often avoid the need for expensive model training
Always consider the full product life cycle costs not just initial training costs
Build composable stacks using large language models as one component rather than a single end-to-end solution
Design solutions focused on humans, explainability, and guarding against hallucinations
Thin wrappers around existing services provide little competitive advantage
Combine large language models creatively with other AI solutions as agents
These models are powerful but be aware of their limitations in hallucination and deployment
Use pragmatically to build solutions that provide real value
Transcripts
hello everyone
um I'm Amir and
it's fantastic to see some some familiar
faces after after years I I just saw
um some some some very good friends here
I'm excited to share uh this with you
and this is the right time so what I'm
going to to talk about today is
um
large language models are they real uh
are they hyped and uh how how we can use
them what should we pay attention to if
we want to build something useful with
them
and I'm going to be very pragmatic about
it and I'll tell you what what exactly I
mean by pragmatic
um so
I'm heading up data science and Ai
adarteria and what we do is we are using
similar techniques and similar science
and engineering that I'm showing you
here to a make documentation Easy by
making any unstructured data
understandable searchable and con and we
convert unstructured data to
knowledge graphs basically
and then we take that and we enable the
users to to to use it for making
recommendations for reasoning on it and
and using it to to create documents and
to
um to do difficult things that are
difficult for humans and they don't need
to be difficult for humans in 2023.
um so
um large language models if you check
Wikipedia you see that a lot of language
models are basically
um a very wide range of models what the
community is called calling large
language models today
is a subsetter that and it is uh
basically a deep learning based uh model
for language
um that has many many parameters
millions or billions or even more
parameters and they are first
pre-trained on a huge Corpus of
unlabeled text so because we are going
to come back to this for for uh
practical reasons in in a few slides let
me walk you through the process
um this is the first step in in in
creating these models this is where you
take a huge model a huge language model
and you train it on a massive Corpus of
unlabeled text in an unsupervised way
then
and and and this is a generative model
when we say large language models today
in almost all the talks these are
generative models so they learn to
predict the next word and then so you
pre-train it then you teach them to use
to follow instructions and then you
align them with with your values
and and then this this is what you use
and and you can train them to follow
instructions or you can train them to be
chat dialogues and uh and and things
like that and then
when you use it when we talk about
further fine-tuning this is when you
take all of this and then you uh
fine-tune it on a
smaller data set in a supervised way to
to make it useful for your domain
your purpose and build something that
you want better
um
there's a lot of
um chat going on in the community and
there are two camps some people think
that this is this is a total height it's
very expensive you should avoid it
because it hallucinates it it makes
things up and uh and on the other side
there are people who believe that this
is it we have the final solution and NLP
is done machines are going to take over
and and we should just sit there and use
them and and uh and that's it and
there's going to be no
no scientists in the future there's
going to be no no programmers in the
future but what is our approach
um what I believe is that we are
somewhere in the middle uh these things
are pretty real you can do you can use
them for very useful applications and
you will see uh those applications in
the talks that we have today and and
they are very powerful but we are in an
active area of research and uh if you're
not familiar with research and how how
research is done and how to deal with uh
with imperfection and how to deal with
the uncertainty that is inherent to uh
to doing research
then you would expose yourself to a huge
risk because you read a paper before
it's peer-reviewed and you think this is
the thing and you pick it and um and and
then you expose yourself to uh massive
failures
our approach is going to be totally
pragmatic we say all right these we know
they are imperfect but can we build
something useful with them and how and
what should we pay attention to if uh if
we want to use them
um and this is not about research
directions
if you want to do research you should uh
completely
um go in an orthogonal Direction here
and take risk and and that that is a
completely different story and that's
not what we're going to talk about all
right so let's first look at some some
amazing success stories of of these
large language models
uh you can use them as
um as writing eight they can be your
assistance in writing they will help you
write correctly but they will not
help you write accurately
and to know more about it see amir's
heart at noon today he will walk you
through using it to to self write a book
a beautiful uh thing and I think this is
one of the main applications of these
tools and
um I personally am using them a lot for
uh for this purpose
another one is you you can use them as
your coding co-pilot they can generate
code and they're very good at it and the
reason is
programs are structured and um and and
you can learn it and they are well
formatted and you can build uh tools
that help you write better code much
faster
again this is a co-pilot it's not
um it's not an autopilot and if you use
it correctly for those who have been
using it correctly this enables you to
do a lot in a in a much shorter time
correct
uh this one if you go to to Aladdin's
talk uh next this is the next talk
coming he will walk you through
um all of it and we'll show you how to
use agents and
um how how you can use large language
models to code and uh and and he will
talk about a lot more as well
then
you
can use them this is kind of a future
but it's actually today as well you can
use them to change the way you use your
data and you talk to your data you can
actually talk to your data you can use
your own language
um for for programming
and
um for example databricks release their
Pi spark AI package recently and uh and
you can see that you can say what you
want to do with your database and and
then it goes and generates code and and
creates agents and it it use it does for
you what's what you need to do
um another one is pandas AI where you
can you can use it to talk to your
pandas in Python uh very interesting and
and Gabriel is going to to talk about
this uh later today
another very interesting application is
you can chat with your data with your
unstructured data in whatever structured
or unstructured in whatever format they
are and this is a very interesting
application
um you can apply to PDFs to YouTube
videos to audio to name it whatever you
can imagine your data to be in you can
you can use it to talk to it and for
example here's an example of
um asking
uh what is what is important about this
paper and uh and you get the result and
um Dennis is going to talk more about
this aspect of stuff today
and you will learn a lot more
interesting aspects of this
now so that's fantastic right it it
looks like we have managed to build
something that is completely automated
with very little effort what I showed
you you can you can do with very little
effort if you use the right model and
the direct framework and it looks like
if you have seen the modern times it
looks like the beginning of the scene
where they introduce the machine
completely automated but then if you
have been reading the news you see
things like this that
meta releases Galactica and then
it has to uh take it down after only
three days
well
then then Google releases Bard and then
the the shares fall
and more recently lawyers use chat GPT
and then
um interesting stuff happens and and
they they have to defend themselves
um and now
so suddenly it looks like the thing the
machine that we thought was working
perfectly is uh is not perfect and the
question is
what happened and uh what is what is
going on with this
I want to talk about this the first
thing and then
um hopefully everyone uh will uh we will
agree quickly and and then we can move
on to the to the next
um stuff
what happened here is called
hallucination and apparently there is a
better word for it uh called fabulation
which is a more psychological term
um these models are prone to making
things up
and because these are Auto regressive
models they
they generate
sequences that sound per that sound like
perfect English but there is but they
are factually wrong
um this is my experiment I asked Chad
GPT whether Einstein could have seen the
could have heard the news about landing
on the moon and when you read it in the
first read it it sounds perfect but then
you are like wait a second what and then
you you read it again and you see that
this is actually
um factually wrong
well
maybe it's because they don't have
access to to the internet what if we
give them access to the internet so I I
used you.com
and I asked about myself the model knows
about me iranian-american astrophysicist
okay close that's that's good work done
CMB but gets everything else wrong so
where I worked and what I did and it
thinks that I I want a grouper prize in
cosmology in 2018 I wish it was true but
unfortunately it's not so
um these
might have fixed by now if you if you
try it again it may not give you the
same answers but that's not the point
what I want to show you is that these
models can make things up and this is a
known fact and it's because of the
nature of these models and we should be
aware of it and when we build something
we should take that into account
all right so
the first rule is if you're going to use
it make sure that you minimize your uh
exposure to the impact of hallucination
and there are ways of doing that this is
an area of research is very active and
there are tools and ways of doing it we
don't have time to go into it but you
should be aware of that
if you compare it with self-driving cars
uh we are around here so we have things
that work and they are very powerful and
very promising and you can you can
actually use them but you cannot put it
on autopilot completely or they will
drive you into a plane and and they will
you know you you will be
um damaged
and um the the right way of using them
is to make sure that you put God razor
on them and you put humans somewhere or
you put fact checking or again uh other
other ways but we're around here and
it's very promising
all right now instead of talking about
rules and do this and uh you know you're
missing out if you're not doing these 15
things and uh uh useless conversations
like that let's build a framework of
thinking that would help us uh think
about large language models and and
figure out what to do when and how
foreign
so the first question is do I really
need an a large language model to solve
my problem
um well the first thing to to try and to
be very honest with yourself about is
a large language model actually solving
my problem and by solving we mean what
is what is your desired metric
take it and run it on your task
experiment with it this is a machine
learning solution and we know how we how
how how to use machine learning
Solutions
so take it and um and and do an honest
statistical
um benchmarking on on your use case and
see if it if it solves your problem
and uh when you do it when I say use it
I mean take it use it zero shot orbit in
context learning and then if you have to
do fine tuning then then think about it
and and ask yourself do I really have to
do fine tuning or can I do better
prompting and in context learning to
um to to get the best out of what I have
the second one is okay if it solves my
problem can I really take it to
production
and we will talk about taking things to
production because these are massive
beasts these are these are huge and
there are considerations when you think
about taking these to production then
think about how flexible you are how
much money you have okay and how much
money you're willing to spend on the
cost of inference let's say you're
you're not training or fine-tuning but
the inference cost could be very high
and and you should be aware of it and
you should
you should be you should have a good
idea about what you want to spend on and
uh how flexible are you about latency
are you are you going to use them for
sub Second Use cases well that's a
different story or can you if you're if
you're writing a book with it you will
be fine waiting for even five minutes
for it to to to generate stuff for you
um and how bad would it be if it starts
making things up do you have their guard
rails in place or are you exposing
yourself to huge uh risk of failure and
um now how to answer these questions is
actually very easy we are in 2023 thanks
to hugging face
you have access to a lot of these large
language models the open source models
are available to you you can uh you can
go there call them use their uh use them
to to solve your problem and experiment
with them
um and you don't need anything else
basically you just you just need to know
how to how to load them and and use them
for uh zero shot or in context learning
uh something very useful that I
recommend to everyone is GPT for all
this is a tool that
um I have it on my machine I use it a
lot and and also you can keep install it
and use it for a lot of your prototyping
and experiments
and um all the Enterprise solutions they
have playgrounds they give you free
access to uh to their stuff so you can
go there and and experiment with it and
and you know quickly get to the bottom
of this question do does it does it
solve my problem yes or no and uh and
then make a decision and then also on
this side ask yourself
can I solve it with a smaller Foundation
model you know because Foundation
Foundation models are you have a huge
family of foundation models and you know
our own bed and and all of its
descendants and everything else that you
have there you have very powerful model
there and a lot of solution a lot of
problems you can actually solve with
with zero shot if you know how to how to
work with this stuff or you can build
few short Learners and and use them to
solve your problem
or you can fine tune them and you can
fine tune a much smaller models of
magnitude smaller and cheaper but then
you have to pay Upfront for fine tuning
and then you do your calculation and you
decide what is right and and what
actually works for you
um here
my recommendation is Occam's Razer and
think about the simplest solution that
is going to solve your problem remember
you have to you have to send these to
production and you have to maintain them
and
um and and picking simple goes a long
way
yeah
now how do you pick it
do should I use open source or closed
Source I look at all the benchmarks
and I get confused and and all of that
but
um what is important is again
experimenting and figuring out what
actually solves your problem I would say
ignore all those benchmarks if you can
and do a little
um statistical testing for yourself
because you know what problem you want
to solve
there are models that are
not on that leaderboard they are not
high on that leaderboard but they
actually can solve your problem well and
they are very small and and you can use
them
um how important is privacy to you and
ownership of your model can you send
your data to a third party or not do you
want your results to be reproducible and
how much you want to pay for it uh these
are important factors that would tell
you whether you can
use closed source
models or or whether you need to take
open source and make it your own and
deploy it in your environment to use it
and then if you if you decide you want
to use open source large language models
then make sure you understand the
license
uh this is a bit tricky it's open source
but it doesn't mean that you can use it
for business and uh make sure you
understand that and pay attention to the
quality of the results see what solves
your problem
pay attention to the size because size
bigger size means higher inference cost
and
um and and higher latency
is going to talk about the whole
landscape and he is going to walk us
through how to think about it and how to
pick and uh what to do there
all right so I should be using the
largest model right and I will be
missing out if I if I don't do it
actually uh site matters and there is a
very interesting paper that
um I recommend reading shows you how
size affects your your result basically
the
essence of it is bigger models or better
Learners and they can do better with
fewer data so you can they begin models
are better few shot learners
and uh that means if you use bigger
models it's more likely that you can
solve your problem with less data but
when what we're talking about here if
you look at this uh this core this is
the super glue score on
on some large language models we are
talking about 70 percent
score
if you are in this business you know
that you cannot sell 70 percent
um if your F score is 70 uh your clients
are not going to pay for it okay that's
why you put a lot of effort in to to go
that last Mile and push it and fine-tune
your models and get to the around 90
percent Mark where you can actually
compete with humans and you can do
something that that people are willing
to pay for so
don't forget that it's important
um with these models you can build
things that kind of work
and
uh and and using it in production for
real stuff is a different story
um a a very interesting uh case study
has been done by refuel and they looked
at labeling using Elements which is
basically a zero shot application the
the reason I'm showing it here is to
show you that look 25 is actually a tiny
model compared to these these massive uh
models and if you look at how it is
doing it's actually doing pretty good
and if you look at the cost per label
it's the cheapest
and uh in terms of latency it's not it's
not too bad
um so
think about it
and and don't go after the biggest most
expensive slowest ones and again asked
what actually solves your problem and
again when you think about benchmarks
take them with use them with care
because first of all uh what has a high
Benchmark on the leaderboard doesn't
necessarily mean that that's the best
thing that solves your problem and
um
evaluations for large language models
we're still learning about it we are
still there is uh there is an active
area of research doing that and also if
a model is closed and they are not
telling you what they have been you uh
what what they have used to train the
model and your benchmarks are available
on the internet
um just think about it that they the The
Benchmark data might have been used in
training of these models okay so again
test figure out for yourself
when you deploy the stuff
um
you so let's say you build it now you
have a solution you want to take it to
production so you need you need to
deploy them or if you're using a third
party then you need to be able to call
the API
um so
when to to use which one
um if you want to use the third party
you need to make sure that you are fine
with with your business is fine with
sending you data to a third party
usually that's if that's a big red line
and you stop there uh for us it's a it's
a it's a noble we can we can do it for
example at arterio
um so then what you need is to have to
build your own mlops team and then you
build a and and you optimize your
pipeline and you optimize your models uh
the second one is very important taking
a model in its raw form and deploying it
uh is is fine but it's not the best you
can do you can get much better latency
much better cost
if you do deep learning engineering on
your models and quantize them and plune
them and use diversity and
um gglm there's a lot of uh there's a
lot you can do there and there are good
um there are good ways of using and
deploying these models uh hugging face
inference databricks base 10 gives you
out of the box easy
um deployment Solutions and AWS is
coming up with chips that are specific
for for inference and and they are
um they are optimized for that and Intel
is releasing very interesting stuff
um to combine hardware and software for
for optimized inference
um RB and financial are going to talk
more about different aspects of this
later in the in the afternoon so don't
miss it
all right and here is something very
interesting that I was impressed with I
thought you would enjoy seeing as well
uh here is me uh running a large
language model on my laptop so if you
think when you when you use a large
language model you need massive gpus and
distributed and cloud and all of that
that's that's not the case you can
actually uh use them on your laptop
today and uh and that's that's huge it's
great for prototyping it's great for a
lot of experiments and for having a
little assistant next to you this is
possible thanks to ggml
all right
do you need
to train your model your own large
language model or
can you build useful stuff with the with
the existing models
if you want to train your own mother
think about it ask yourself is it really
necessary or am I thinking about
training because I'm used to doing
machine learning that way and and I
think if I want to do something of my
own I have to train my model
and the reason you should think really
hard about it is because these models
are massive and in addition to a lot of
compute a lot of GPU TPU time you also
need a lot of data to be able to build
something meaningful there so ask
yourself do I have the data do I have
the budget do I have the time do I have
the right team and do I really have do I
really need to to train my own or or can
I
use in context learning with good
prompting or maybe a little bit of
fine-tuning
and uh and and think about
when you it's not when you train it's
not a one-time thing you have to train
and retrain and retrain and uh and and
the cost uh quickly adds up and think
about what actually you you gain if you
if you do that
um
so here I wanted to say
um again most of your problems most of
the problems that I have seen you should
be able to do with uh within context
learning and um and you know using
something like ESP or or something
similar is going to
solve a lot of your problems and then
your large language model could be one
component in your whole stack we will
talk about it in a second and
if you start getting into this phase of
no I have to fine tune and I have to
collect data and I have to do it a
supervised training to to improve the
resource and get what I want then take a
step back and look at it again and ask
yourself
um is my problem best solved with a
large language model or since I'm I'm
going to fine tune can I build something
which is way smaller and and I fine-tune
it and and I I create something that is
fast and efficient and solves my problem
at a a fraction of that cost
um
and here is my uh the next two slides
are two
um
to two recommendations that I have for
you if you if you're doing this
use the power prompting use in context
learning and push it to to its limits
and
um
make sure that that you know how to do
this efficiently that solves a lot of
your problems
and always think about your your your
stack your your Hardware
imagine
and know that that you might be able to
to do a lot of things without using the
most expensive
um Hardware
and if you
if you do a little bit of deep learning
engineering on your model and if you
think about it right you my experience
is that you're always able to find a
solution that is cheaper faster and uh
and remember inference costs are
different from training costs training
cost is a one-time thing in the
beginning
and then you're good for for a while
inference cost
you're paying it every time someone
called your API and they add up quickly
and they make it expensive and and that
cost brings your margin down and then
then you will end up building something
that works and is interesting but you're
breaking the bank and you can like not
make money with it and it's not
sustainable and your idea dies and your
product doesn't go anywhere and uh be
composable build a Tiramisu at arteria
we love to build pyramid suits because
we love kiramisu
um I think the all of these different
things as layers in your Tiramisu and
use a large language model as one
component but remember that you can add
many more layers on top of it to make
something of your own
and and make a difference and
always think of it as a product as
opposed to a jupyter notebook project
and
um
design design is very important for this
stuff make sure that you design it in a
way that protects you from hallucination
that puts the human in the center that
makes it explainable makes it
understandable and if you do it right
you will build something that will make
you very rich uh very quickly
and this is
my my final thought on this
I think
if you build a thin layer which is a
wrapper around existing Services
you will be able to build something very
quickly in hours or days and and it will
be impressive but you will not have that
competitive advantage to anyone else
because remember what you did was smart
but now that you have done it it takes
others also a few hours or a few days to
catch up with you
and then then you have you're just one
voice in
um this whole Market
think more deeply about it and start
building more sophisticated things and
um and and
one interesting use of llms
which I think is the the right so the
right question to ask today is how can I
use different AI components together to
build something that actually makes a
difference
not whether I should use LL or I should
not use elements this is this is another
flavor of machine learning models
and they can be combined with other
things you can build change with them
you can you can use them as agents and
you can combine with your let's say you
have your best named entity recognition
model that that is better than everyone
else in the world
you can use uh these large language
models as the base and then you can use
them as agents and you can have them use
your tool your model your other models
as tools and then you can see that you
quickly start building uh things that
are sophisticated and are very difficult
to to beat and compete with
and
um
so that's
it basically
um these are very powerful tools
you should use them for sure
but be aware of
uh everything that that we talked about
including hallucination and deployment
considerations and pay attention to
where you can or you should or you
should not use open versus close models
and definitely use them as writing AIDS
definitely use them as your co-pilots
for writing code and for low data
regimes and
I hope you build something
exciting and I'm looking forward to
seeing everything that might happen
after this Workshop
Voir Plus de Vidéos Connexes
Introduction to large language models
AT&T Presents - Winning in a GenAI Disruptive World
Foundation models and the next era of AI
2-Langchain Series-Building Chatbot Using Paid And Open Source LLM's using Langchain And Ollama
The Fastest Way to AGI: LLMs + Tree Search – Demis Hassabis (Google DeepMind CEO)
AI and Kotlin: A Perfect Mix | Vladislav Tankov
5.0 / 5 (0 votes)