Unlock the secrets of AI's transformative powers for businesses with our latest episode! Discover how fine-tuning large language models can enhance your business operations, much like training a super intern to become an industry expert. We promise to unravel the complexities of Retrieval-Augmented Generation (RAG), offering you insights into how it equips businesses, especially medium-sized ones, to access real-time, updated information and stay competitive in fields with changing dynamics like UK immigration law.
Join us as we uncover AI's unexpected role in personalized learning and development, revolutionizing traditional training methods. Whether you're looking to tailor learning paths for your sales team or enhance overall engagement and effectiveness, AI offers solutions that elevate job roles rather than replace them. Learn how fine-tuning AI with valuable resources like product documentation and insights from top performers can drive business growth and solve real challenges. This episode is your gateway to understanding the dynamic potential of AI in fostering both personal and professional development.
Episode Transcript
Welcome back everyone to the Deep Dive.
You know we talk a lot on theshow about the incredible power
of AI.
Speaker 2 (00:05):
Yeah.
Speaker 1 (00:06):
And how it's changing the game for businesses of all
sizes.
Speaker 2 (00:08):
Yeah.
Speaker 1 (00:09):
But I think for a lot of business owners it can still
feel like this mysterious,almost intimidating thing, you
know, like how can I someonewho's focused on running my
business actually use thistechnology to my advantage?
Speaker 2 (00:21):
Yeah.
Speaker 1 (00:22):
And that's what we're diving into today.
We're talking about fine tuninglarge language models, or LLMs.
Speaker 2 (00:29):
Yeah.
Speaker 1 (00:30):
For your business.
Now, before you tune outthinking this is way too techie
for you trust me, it's not let'sbreak this down.
Speaker 2 (00:36):
Yeah.
So imagine you've just hired anew intern, right?
Okay, this intern.
They're bright, they're eager,they've read every book
imaginable on just about anytopic.
Speaker 1 (00:45):
Okay, a super intern.
Speaker 2 (00:47):
A super intern.
They're good at general tasksresearch, writing, summarizing,
you name it.
Speaker 1 (00:52):
So like the perfect entry-level employee.
Speaker 2 (00:55):
Yeah, but there's a catch they don't have any
specific experience in yourindustry.
Speaker 1 (00:59):
Ah, I see, so they might not understand the nuances
of, say, the handcraftedfurniture business.
Speaker 2 (01:06):
Exactly.
They can tell you aboutfurniture in general, but they
wouldn't know the differencebetween I don't know like a
mortise and tenon joint and adovetail joint, and those are
things your customers, who arepassionate about handcrafted
furniture, would really careabout.
Absolutely.
That's where fine tuning comesin.
It's like taking that superintern and sending them to a
specialized training programfocused entirely on your
(01:29):
business and your industry.
Speaker 1 (01:30):
So, instead of giving them a general business
textbook, we're giving themchapters specifically on our
company, our products, ourprocess.
Speaker 2 (01:37):
Precisely.
You're giving them theknowledge they need to become a
true expert in your field.
Speaker 1 (01:42):
And man, they can use that knowledge to help you in
so many ways, oh absolutelyAnswering customer questions
with real expertise, maybewriting incredibly targeted
marketing copy.
Even helping with designelements based on your specific
style.
That's right.
So, okay, I'm starting to getthe picture here, but fine
tuning, it sounds prettycomplicated.
Is this something that'sactually achievable for you know
(02:05):
, a regular business.
Speaker 2 (02:06):
Oh, absolutely.
It's definitely more involvedthan just using a generic LLM,
but it's not this like mysticalprocess.
There are clear steps involved,from gathering the right data
to training and testing themodel.
Speaker 1 (02:18):
Walk me through it.
Let's say I run a consultingfirm and I want to fine tune an
AI to be an expert in I don'tknow UK immigration law.
Speaker 2 (02:27):
Okay, perfect example .
So first you need to gather allthe relevant information you
can on UK immigration law Thinkofficial guidelines, legal
precedents, successfulapplications.
Speaker 1 (02:36):
A massive library basically A trip.
Specifically on UK immigration.
Speaker 2 (02:39):
Exactly, and then you use that library to train the
LLM.
This is where it really startsto learn the language, the logic
, all the nuances of the field.
Speaker 1 (02:48):
It's not just memorizing facts, it's
understanding the law itself.
Speaker 2 (02:51):
Precisely, and then you test it rigorously to make
sure it's giving you accurateand reliable information.
Speaker 1 (03:02):
If it's not performing well, you can adjust
the training or give it evenmore data to work with.
That makes sense.
But here's a question whatabout when the information
changes, Like UK immigration law?
I imagine it's updated prettyoften.
Fine tuning seems like it wouldbe more permanent.
Speaker 2 (03:14):
That's a great point, and you're right.
For things that are constantlyevolving, there's another
approach called RAG.
Speaker 1 (03:19):
RAG like an old piece of cloth.
Speaker 2 (03:22):
Yeah, exactly, but in this case it stands for
Retrieval, augmented Generation.
Speaker 1 (03:26):
Okay.
Speaker 2 (03:27):
Imagine your fine-tuned AI.
It has access to this massive,constantly updated library,
right?
Speaker 1 (03:33):
Okay.
Speaker 2 (03:34):
So it's not just relying on its initial training.
It can also pull in the latestinformation from outside sources
when it needs to.
Speaker 1 (03:40):
So like it has a research assistant who can grab
any new document or update righton the spot, Exactly that's
what makes Argi so useful.
Speaker 2 (03:48):
For things like customer support, where policies
or product details might changefrequently, the AI can always
give the most up-to-date answer,even if it wasn't specifically
trained on that change.
Speaker 1 (03:58):
So, to sum it up, fine-tuning gives you deep
expertise, that's consistent.
But Argi gives you thatflexibility for when things are
always changing.
Speaker 2 (04:06):
Exactly and which approach is right for you.
Well, that depends on yourbusiness and the type of
information you're dealing with,but, either way, both fine
tuning and RAG offer some reallyincredible opportunities to
transform how your businessoperates, and you know we've
been talking about how all thiscan be used in a business.
But I think it's reallyimportant to think about how
this applies especially, youknow, to those medium sized
(04:29):
businesses.
Speaker 1 (04:30):
Right, because they're not a tiny startup, but
they also don't have the sameresources as a huge corporation.
Speaker 2 (04:36):
Exactly.
Speaker 1 (04:37):
So, like that ARAG thing we were just talking about
, being able to access the mostupdated information.
I feel like that would becrucial for them.
Speaker 2 (04:44):
Oh, absolutely Imagine you wouldn't want your
sales team out there pitchinglast year's prices, would you?
Speaker 1 (04:49):
Definitely not.
Things change so fast thesedays.
Speaker 2 (04:52):
All the time.
So, yeah, our ag can be alifesaver there.
But it's not just about keepingup to date, it's about you know
doing things better and fasterand more efficiently overall.
Speaker 1 (05:01):
Okay.
So more than just not fallingbehind, it's about getting ahead
.
Speaker 2 (05:05):
Exactly.
Think about your marketing team.
They're trying to put togethera new campaign.
They have to look at all themarket trends, what the
competitors are doing, all thecustomer feedback.
Speaker 1 (05:14):
Yeah, and that can be incredibly time consuming.
Speaker 2 (05:17):
Absolutely, and especially for a medium-sized
business, that they probablydon't have a huge team to handle
all of that.
Speaker 1 (05:23):
Resources are stretched thin already.
Speaker 2 (05:24):
Exactly.
But imagine if you couldfine-tune an LLM on all of your
past marketing campaigns, onyour customer data, on industry
best practices and it could gothrough all of that information
in a fraction of the time.
Speaker 1 (05:39):
So like a marketing consultant who knows your
business inside and out but whoworks at you know super speed.
Speaker 2 (05:44):
Yeah, and who's available 24 seven?
Speaker 1 (05:47):
Wow, that would be amazing.
So, okay, we've talked aboutthe benefits, but I'm curious
can you give me a specificexample, like how would a medium
sized business actually usefine tuning to solve a real
problem they might be facing?
Speaker 2 (06:00):
Sure, let's say you have a manufacturing company and
they make I don't knowspecialized components for the
auto industry.
They get these complex ordersall the time, you know, with
unique specs, really tightdeadlines.
Right now they probably have ateam that spends hours going
through each order making surethere aren't any errors, you
know, making sure it canactually be manufactured.
Speaker 1 (06:20):
That sounds incredibly time consuming and
like a lot of room for error.
Speaker 2 (06:24):
Oh, absolutely.
But imagine now they fine tunean LLM on their entire order
history, their manufacturingblueprints, all the industry
standards, and now this AIassistant can look at a new
order in seconds, flag anypotential issues, you know,
maybe even suggest the mostefficient way to manufacture it.
Speaker 1 (06:43):
So like an extra set of eyes, but eyes that have,
like superhuman, attention todetail.
Speaker 2 (06:48):
Exactly.
And now your team.
They can focus on the biggerpicture, you know, process
improvement, innovation.
Speaker 1 (06:55):
I'm sure our listeners are thinking, okay,
this all sounds incredible, butwhere do I even begin?
Speaker 2 (07:00):
Well, the first step is to really think about what
are those areas in your businesswhere fine tuning could make
the biggest difference.
What are the things that aretaking up a lot of time, or
where errors are costing youmoney or that just require a
really high level of expertise?
Speaker 1 (07:16):
OK, so things like data analysis, maybe contract
review, customer support, maybeeven content creation.
Speaker 2 (07:23):
Exactly.
Once you know where you want toapply it, then you can find a
company like 686 thatspecializes in this and can
guide you through the process.
Speaker 1 (07:32):
Now I know some business owners.
They might be a little hesitantabout all of this.
Speaker 2 (07:36):
Oh yeah.
Speaker 1 (07:36):
They might be thinking this is too expensive
or I'm not techie enough forthis.
Speaker 2 (07:40):
Yeah, I understand that hesitation, but the truth
is AI is not some futuristicconcept anymore.
It's here and it can be adaptedto fit any budget, any level of
technical skill.
In fact, you know, ignoring AIthese days, that's going to be
more costly in the long run.
Speaker 1 (07:57):
Right, because you can't afford to fall behind your
competitors who are embracingthis.
Speaker 2 (08:01):
Exactly, and when you think about all the benefits
we've talked about, you knowincreased efficiency, less
errors, better decision making,the return on investment for
fine tuning it can be massive.
Speaker 1 (08:12):
So it really sounds like, especially for these
medium sized businesses, finetuning can be a way for them to
really punch above their weight,you know.
Speaker 2 (08:19):
Yeah, that's a great way to put it.
It's kind of leveling theplaying field a little bit right
, giving them access to thesepowerful tools that you know
maybe before only these hugecompanies could really afford to
use.
Speaker 1 (08:35):
And we've talked about so many examples, from
marketing to customer service,even to manufacturing but are
there any like unexpected waysthat businesses are using fine
tuned AI, like things peoplemight not think of right away?
Speaker 2 (08:46):
Oh, absolutely.
You know.
One area that's starting toreally take off is using it for
personalized learning anddevelopment.
Oh, interesting so imagine youhave a sales team right and they
all have different levels ofexperience, different product
knowledge.
Speaker 1 (08:59):
Right.
Some are brand new and havebeen there forever.
Speaker 2 (09:02):
Exactly and traditionally you might have
like a one size fits alltraining program, but we all
know that doesn't really workthat well.
Speaker 1 (09:09):
Yes, some people are bored because they already know
it.
Some are lost because they needmore basics.
Speaker 2 (09:12):
Exactly.
But what if you could fine tunean AI on all of your product
documentation, your salesplaybooks, recordings of your
top performers, even Okay?
And now it can create apersonalized learning path for
each person on your team.
Speaker 1 (09:28):
So the new person gets a crash course on the
essentials the veteran canreally hone in on.
You know those advancedtechniques.
Speaker 2 (09:36):
Exactly.
It's all about giving them theright information at the right
time to really maximize theirlearning.
Speaker 1 (09:41):
That's incredible and it's a great example of how
this isn't about AI taking overjobs.
It's about using AI to makethose jobs more engaging, more
effective.
Speaker 2 (09:50):
It's about using AI to make those jobs more engaging
, more effective.
It's a tool and, like any tool,it can be used for good or for
less good things.
It's really about how we chooseto implement it.
Speaker 1 (10:00):
And I think it's really exciting to think about
all the positive ways it can beused to solve problems, to help
businesses grow, to just makeour lives a little bit easier.
Speaker 2 (10:09):
I agree.
I think we're just scratchingthe surface of what's possible
with fine-tuned AI, and I can'twait to see what the future
holds.
Speaker 1 (10:17):
And for our listeners who are ready to dive into that
future for their own businesses.
Speaker 2 (10:22):
Contact 686.
They can tell you more, walkyou through it step-by-step or,
if you want, do it for you.
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