RAG vs Fine-Tuning Explained
Key Points
- Retrieval‑augmented generation (RAG) lets a pre‑trained LLM pull up‑to‑date, domain‑specific documents (e.g., PDFs, spreadsheets) at query time and augment the prompt, avoiding hallucinations without any model retraining.
- Fine‑tuning involves actually re‑training the base LLM on a targeted corpus so the model internalizes specialized knowledge, making it natively proficient in a particular domain.
- RAG is ideal when you need quick, accurate answers from confidential or proprietary data and want to keep the underlying model unchanged, while fine‑tuning is better for deep, permanent specialization where inference speed and reduced latency matter.
- Choosing between them depends on factors like the freshness of required information, the volume and sensitivity of your data, development resources, and whether you prefer on‑the‑fly augmentation (RAG) or a permanently tuned model (fine‑tuning).
Full Transcript
# RAG vs Fine-Tuning Explained **Source:** [https://www.youtube.com/watch?v=00Q0G84kq3M](https://www.youtube.com/watch?v=00Q0G84kq3M) **Duration:** 00:08:51 ## Summary - Retrieval‑augmented generation (RAG) lets a pre‑trained LLM pull up‑to‑date, domain‑specific documents (e.g., PDFs, spreadsheets) at query time and augment the prompt, avoiding hallucinations without any model retraining. - Fine‑tuning involves actually re‑training the base LLM on a targeted corpus so the model internalizes specialized knowledge, making it natively proficient in a particular domain. - RAG is ideal when you need quick, accurate answers from confidential or proprietary data and want to keep the underlying model unchanged, while fine‑tuning is better for deep, permanent specialization where inference speed and reduced latency matter. - Choosing between them depends on factors like the freshness of required information, the volume and sensitivity of your data, development resources, and whether you prefer on‑the‑fly augmentation (RAG) or a permanently tuned model (fine‑tuning). ## Sections - [00:00:00](https://www.youtube.com/watch?v=00Q0G84kq3M&t=0s) **Choosing RAG vs Fine‑Tuning** - An overview of how Retrieval‑Augmented Generation and fine‑tuning each strengthen large language models, their ideal use cases, and guidance for selecting the right approach in enterprise applications. ## Full Transcript
let's talk about rag versus fine-tuning
now they're both powerful ways to
enhance the capabilities of large
language models but today you're going
to learn about their strengths their use
cases and how you can choose between
them so one of the biggest issues with
dealing with generative AI right now is
one enhancing the models but also to
dealing with their limitations for
example I just recently asked my
favorite llm a simple question who won
the Euro 2024 World Championship and
while this might seem like a simple
query for my model well there's a slight
issue because the model wasn't trained
on that specific information it can't
give me an accurate or up-to-date answer
at the same time these popular models
are very generalistic and so how do we
think about specializing them for
specific use cases and adapt them in
Enterprise applications because your
data is one of the most important things
that you can work with and in the field
of AI using techniques such as rag or
fine-tuning will allow you to
supercharge the capabilities that your
application delivers so in the next few
minutes we're going to learn about both
of these techniques the differences
between them and where you can start
seeing and using them in let's get
started so let's begin with retrieval
augmented generation which is a way to
increase the capabilities of a model
through retrieving external and
up-to-date information augmenting the
original prompt that was given to the
model and then generating a response
back using that context and information
and this is really powerful because if
we think back about that example of with
the Euro Cup well the model didn't have
the information in context to provide an
answer and this is one of the big
limitations of llms but this is
mitigated in a way with rag because now
instead of having an incorrect or
possibly um a hallucinated answer we're
able to work with what's known as a
corpus of information so this could be
data this could be PDFs documents
spreadsheets things that are relevant to
our specific organization or knowledge
that we need to specialize in so when
the query comes in this time we're
working with what's known as a retriever
that's able to pull the correct doc
doents and Rel relative context to what
the question is and then pass that
knowledge uh as well as the original
prompt to a large language model and
with its intuition and pre-trained data
it's able to give us a response back
based on that contextualized information
uh which is really really powerful
because we can start to see that we can
get better responses back from a model
with our proprietary and confidential
information without needing to do any
retraining on the model uh and this is a
great and popular way to enhance the
capabilities of a model uh without
having to do any fine-tuning so as the
name implies what this involves is
taking a large language foundational
model but this time we're going to be
specializing it in a certain domain or
area so we're working with labeled and
targeted data that's going to be
provided to the model and and when we do
some processing we'll have a specialized
model for a specific use case to talk in
a certain style to have a certain tone
that could represent our organization or
company and so then when a model is
queried from um a user or any other type
of way we'll have a a response that
gives the correct tone and output or
specialty in a domain that we'd like to
receive and this is really important
because what we're doing is essentially
baking in this context and intuition
into the model um and it's really
important because this is now part of
the model's weights versus being
supplemented on top with a a technique
like
rag okay so we understand how both of
these techniques can enhance a model's
accur output and performance but let's
take a look at their strengths and
weaknesses in some common use cases
because the direction that you go in can
greatly affect a model's performance its
accuracy outputs compute cost and much
much more so let's begin with retrieval
augmented generation and something that
I want to point out here is that because
we're working with a corpus of
information and data this is perfect for
dynamic data sources such as databases
uh and other data repositories where we
want to continuously pull information
and have that up to date for the model
to use
understand and at the same time because
we're working with this retriever system
and passing in the information as
context in the prompt well that really
helps with hallucinations and providing
the sources for this information is
really important in systems where we
need trust and transparency when we're
using AI so this is fantastic but let's
also think about this whole system
because um having this efficient
retrieval system uh is really important
in how we select and pick the data that
we want to provide in that limited
context window and so maintaining this
is also something that you need to think
about and at the same time what we're
doing here in this system is effectively
supplementing that information on top of
the model so we're not essentially
enhancing the base model itself we're
just giving it the relative and
contextual information it needs versus
fine-tuning is a little bit different
because we're actually baking in that
context and intuition into the model
well we have greater um influence um in
essentially how the model behaves and
reacts in different situations is it an
insurance adjuster can it summarize
documents whatever we want the model to
do we can essentially use fine tuning in
order to uh help with that process and
at the same time because that is baked
into the model's weights itself well
that's really great for Speed and
inference cost and a variety of other um
factors that come to running models so
for example we can use smaller prompt
context windows in order to get the
responses that we want from the model
and as we begin to special these models
they can get smaller and smaller for
specific use case so it's really great
for running these specific uh
specialized models in a variety of use
cases but at the same time we have the
same issue of cut off so up until the
point where the model is trained well
after that we have no more additional
information that we can give to the
model so the same issue that we had with
the World Cup example so both of these
have their strengths and weaknesses but
let's actually see this in some examples
and use cases here so when you're
thinking about choosing between r and
fine-tuning it's really important to
consider your AI enabled application
priorities and requirements so namely
this starts off with the data is the
data that you're working with slow
moving or is it fast for example if we
need to use uh up-to-date external
information and have that ready
contextually every time we use a model
then this could be a great use case for
rag for example a product documentation
chatbot where we can continually update
the responses with up-to-date
information now at the same time let's
think about the industry that you might
be in now fine tuning is really uh
powerful for specific industries that
have nuances in their writing styles
terminology vocabulary and so for
example if we have a legal document
summarizer well this could be a perfect
use case for fine tuning now let's think
about sources this is really important
right now in having um transparency
behind our models and with rag being
able to provide the context and where
the information came from uh is really
really great so this could be a great
use case again for that chatbot for
retail insurance and a variety of other
uh uh uh specialities where having that
source and information in the context of
the prompt is very important but at the
same time we may have things such as
past data in our organization that we
can use to train a model so let it be uh
accustomed to the data that we're going
to be working with for example again
that legal summarizer could have past
data on different legal cases and and
documents that we feed it so that it
understands the situation that's working
in we have better more desirable outputs
so this is cool but I think the best um
situation is a combination of both of
these methods so let's say we have a
financial news reporting service well we
could fine-tune it to be uh native to
the industry of finance and understand
all the lingo there uh we could also
give it past data of financial records
and let it understand um how we work in
that specific industry but also be able
to provide the most up-to-date sources
for news and data and be able to provide
that with a level of confidence and
transparency and Trust to the end user
who's making that decision and needs to
know the source and this is really where
a combination of fine-tuning and rag is
so awesome because we can really build
amazing applications taking advantage of
both rag as a way to retrieve that
information and have it up to date but
fine tuning to specialize our data uh
but also specialize our model in a
certain domain so uh they're both
wonderful techniques and they have their
strengths but the choice to use one or
combination of both techniques is up to
you and your specific use case and data
so thank you so much for watching uh as
always if you have any questions about
fine-tuning rag or all AI related topics
let us know in the comment section below
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content thanks so much for watching