Welcome back everyone.
I'm Mukundan Sankar, your host.
And if you're new here,
let me tell you a bit about myself.
I'm an experienced data professional
and I have been in the industry for about seven years now.
I'm an active researcher in data and AI.
So everything data, including data analysis, data science,
analytical engineering, machine learning,
deep learning, and now AI.
So the roles that I've held and the projects that I've done
have helped me get this far and get these skills.
So I've always been fascinated by how technology
can help us navigate the complexities of everyday life.
And today I'm really excited to share something with you
that's gonna change how we deal with information overload.
It's called retrieval augmented generation or RAG.
So do you ever feel like you're drowning in information,
like basically articles, reports,
just endless amounts of data that make it hard to find
anything that really matters?
Well, you're not alone.
We're living in an era where we constantly bombarded
with more information than ever before.
And it can get overwhelming, right?
Here's the exciting part though.
There's something that can help
with this information overload.
And it is called retrieval augmented generation
or RAG for short.
I know, I know it sounds super technical,
but let me break it down for you so that
when you're understanding why this is important
and why this is seriously a game changer.
So what is RAG exactly?
Well, RAG is an AI model that combines the best
of both worlds, that is retrieval and generation.
So I'll just go with that again.
The best of both worlds combines retrieval and generation.
Let's start with the first part, the retriever.
The retriever is basically doing that retrieval process.
So imagine it like a super powered search engine.
When you ask this RAG a question,
the retriever goes out and scours vast amounts of data,
documents, articles, reports,
to find the most relevant information.
But here's the catch.
It's not just pulling up a list of links or keywords.
No, it's smarter than that.
The retriever is looking for deeper connections,
patterns and context in the data,
like a detective combing through clues to find the truth.
And then once it gathers all the good stuff,
the second part kicks in, the generator.
Now the generator is like an amazing storyteller.
It takes all that raw data that you just gave
and all those facts and figures
that the retriever basically dug up
and it summarizes them for you in a way that's clear,
concise and even a little fun.
You're not reading through pages anymore
and pages of technical jargon or dry statistics.
The generator packages everything
in a way that actually makes sense.
So you might be thinking,
how is this different from what we have now?
Can't I just Google and basically read
the results from Google?
Well, here's the kicker.
This is next level.
You see, traditional AI models
and even your trusty search engine,
sometimes they miss the mark.
They can pull in inaccurate or outdated information
or worse, just make stuff up.
That's what they call AI hallucination.
Basically when the AI goes off script
and creates information that isn't real.
That's scary, right?
But RAG, it's different.
It's grounded in real data.
The retriever makes sure of that.
It doesn't just generate answers
based on what the AI already knows,
which could be limited outdated.
Instead, it brings in fresh,
up to date facts from reliable sources.
This makes RAG way more accurate and trustworthy.
And get this, this isn't just theoretical.
RAG is being used for some seriously cool stuff.
I for one, I have used it to convert text news articles
to audio using LLMs, which is large language models
with this RAG technology.
And I will link the article in the show notes.
So it's basically, just to give you a brief overview
of what that article is.
I've used an advanced large language model
to convert news articles in text form into audio,
basically using a Google text to speech
and GTTS module to convert that.
And it will give you the article in audio form.
That's very interesting.
And that was all possible thanks to RAG.
So imagine, let's say you're imagining
some deeper use case than what I wrote in my blog, right?
A more personalized news podcast, for example.
So what does that even mean?
So basically, instead of just reading these long form
text articles that you get, you get curated summaries,
but in audio form as a podcast, for example.
Right, like, I mean, I know we have like very amazing
newsletters that we get through emails and that's great.
I love that.
But like this is maybe a podcast for you.
That would be amazing, right?
I mean, just think of all the amazing possibilities we have.
So like the AI in this case would be scanning the news,
picking up key points from the articles and boom,
it turns it all into something you can listen to
on your commute.
It's not just about spitting facts either.
The generator makes sure the final output is coherent
and easy to understand, just like a human would explain
it to you, but it doesn't stop with the news.
Think about how this could apply to research papers,
where it is like dense, complicated scientific reports
that would take hours to read and understand.
RAG would summarize the key breakthroughs for you
in minutes.
Or at work, imagine cutting down hours of slogging
through long reports and instead having the AI
highlight the most important points, making meetings
and decision making so much faster and more efficient.
What makes this even more exciting is that RAG
has real potential.
To personalize how we learn and process information.
So instead of wading through mountains of irrelevant data,
RAG narrows it down and presents what's actually useful.
It's like having your own personal research assistant
or librarian working at lightning speed,
delivering exactly what you need when you need it.
So yeah, RAG isn't just another buzzword,
it's here to stay.
It's going to change how we handle information,
how we learn, how we work, and how we make decisions
of the world.
And like any powerful world, any powerful tool,
it's up to us to use it wisely.
Because at the end of the day,
it's not just about managing information,
it's about making it work for you, helping you focus
on what really matters.
So, and that's all for today.
Just like to recap what I just said,
retrieval augmented generation,
or RAG is just truly game changer.
You're going to, instead of having mountains
of information, you know, you're looking through them
personally, RAG would do that for you
and give you more personalized insights
that actually make sense.
It's revolutionizing everything that I just mentioned before.
As we move forward, it's important to remember
that tools like RAG are only as good
as how we choose to use them.
And technology like this, as the power of the brain,
technology like this as the power to shape our future.
And you already know this, but we should use it wisely.
Thanks for tuning in.
And if this has sparked your curiosity,
don't forget to subscribe because we'll be diving
into even more exciting tech trends,
data projects and innovations in the episodes to come.
Until next time, keep exploring, keep learning
and stay curious.
And GeoAI is just getting started.