Welcome to the Deep Dive. Today we're tackling
a concept that's, well, it's rapidly becoming
fundamental if you're serious about making AI
work in the real world. Retrieval augmented generation.
Our rag for short. Yep our rag we were looking
at some material an article really that lays
it out pretty clearly it explains what it is
and Maybe more importantly why it's such a big
deal right now, right? Because everyone's seen
what these large language models can do, you
know the GPT's Paul M Gemini llama Claude They're
amazing with language totally phenomenal generating
text understanding it But and this is what the
source material we looked at really points out
these models powerful as they are They have some
built -in limits, like right out of the box.
OK, let's unpack this then, because they seem
so smart. What are these constraints? What does
the article actually highlight? Well, the text
is pretty direct about the main problems. First
off, their knowledge is basically frozen in time.
Frozen? Yeah, static. It's limited to whatever
massive data sets they were trained on. So if
something happened after that training cutoff
date, the model just doesn't know about it. Oh,
OK. So asking about like market news from yesterday
or some brand new research. It won't have the
latest. Exactly. It's completely unaware. And
the second big one, especially if you're thinking
about using this in a business, is that they
can't access private information. Nonpublic stuff.
Proprietary company knowledge. Right. Internal
reports, databases, specific customer info, that
kind of thing. Precisely. It wasn't in the public
training data, so the LLM can't see it, can't
answer questions based on your internal documents
or processes. So you can't just ask it, hey,
what were the key findings from last quarter's
internal review? Nope, not the base model. And
then there's the third issue the source talks
about, hallucinations. Right, I've heard that
term. They just make things up. Sort of. They're
designed to generate text that sounds plausible
based on patterns they learned. But sometimes
that leads them to state incorrect information
very, very confidently. It sounds totally convincing,
but it's factually wrong. Which is obviously
a huge problem if you need accuracy, especially
with specific current or sensitive internal data.
So, okay, these are pretty big limitations. How
do we actually make these models useful for those
kinds of tasks? Is this where RAG comes in? This
is exactly where the article positions our RAG.
It's presented as a really practical, pretty
powerful way to breach those exact gaps we just
talked about. Ah, okay, giving the LLMs. the
context they're missing. That's the core idea,
yeah. Okay, here's where it gets really interesting
then. If RGAG is the solution, what is it? What
does the source material say retrieval augmented
generation actually means? So the text describes
RGAG basically as a technique, a method to seriously
boost the quality and relevance of a language
model's output. And the key thing is it does
this by feeding the model extra relevant data
along with the user's input without having to
retrain or change the underlying LM itself. So
you're giving it extra info on the fly. Pretty
much. And that extra info, that supplemental
data, it can come from external sources, things
that are kept up to date, or, and this is crucial
for businesses, it can come from an organization's
own private knowledge base, internal documents,
databases, whatever. So instead of the LLM just
guessing based on its old training data, you're
saying, here, look at this specific information
before you answer. That's a perfect way to put
it. You're giving it the specific facts it needs
for that particular question or task. The source
explains Argue as merging two things, a pre -trained
language model, that's the generator creating
the text, and external knowledge index, which
is handled by something called a retriever. Retriever
and generator got it and the text mentions this
approach came out of Facebook AI research It
showed pretty quickly much better results on
tasks that need a lot of specific knowledge like
question answering checking facts It just generated
language that was way more precise and factual
Okay, so it's this combo get the right info first
then generate that makes sense The article digs
into the how right it talks about the architecture
of these two players the retriever and the generator
Let's start with the Retriever. What's its specific
job? The Retriever is basically the smart search
engine part of the system. Its job is to sift
through potentially huge amounts of information.
Could be databases inside a company, documents,
maybe even stuff on the web. And the source mentioned
it can connect to, like, Business systems. Yeah,
exactly. Things like CRMs, ERP systems, cloud
storage, other line of business apps. But its
key function isn't just searching. It's about
intelligently filtering and finding only the
pieces of information that are most relevant
to the user -specific question. Like a super
-efficient librarian finding the exact paragraph
you need. That's a great analogy. And that specific
relevant info it digs up. That becomes the context
that gets passed on. The article actually gives
BingChat as an example of something using this
kind of mechanism to pull in current info rather
than just relying on the model's cutoff date.
Got it. So the retriever fetches the relevant
snippets and then the generator, which is the
LLM, it takes those snippets and what? And it
generates the final answer. The generator, the
LLM, takes that context provided by the retriever
and uses that information to craft the response
in natural language. It bases its answer specifically
on the facts it was just given. So it's grounded
in the retrieved info. Precisely. Grounded. That
ensures the answer is much more likely to be
factual and directly relevant to the query and
the specific context. The article also notes
you can use things like prompt engineering here
to further guide how the LLM uses that context,
something we've touched on before. OK, that high
level idea, find context, then generate based
on it clicks. Can you walk us through the actual
sequence, the step by step the source describes,
like when I ask a question, what happens? Sure.
The text lays out a pretty standard flow. So
it starts when you, the user, submit a query,
ask a question. First, that question, your text,
gets turned into a numerical format, usually
a vector. A mathematical representation. Exactly.
Captures the meaning numerically. Then the Retriever
component takes this vector and uses it to search.
It searches through an index, usually an index
of documents that have been broken down into
smaller chunks. Tunks, like paragraphs or something.
Yeah, smaller pieces of text. It looks for the
chunks whose own vectors are mathematically closest
to the query vector. That closeness means they're
semantically relevant. They're talking about
the same concepts as your question. Okay, so
the math helps find the best matching pieces
from the knowledge base. Right. And once it finds
those relevant chunks, that information is combined
with your original query. Together, they form
what's called an augmented prompt. Augmented,
so it's my question peelers at this extra context
it found. Exactly. It's not just your question
anymore. It's your question supercharged with
relevant facts. And that whole thing gets sent
to the LLM. That's the critical next step. This
augmented prompt query plus context goes to the
LLM. And crucially, the LLM is usually instructed
to generate its response based only on the information
provided right there in that prompt. Use this
context. Don't just rely on your training data.
Precisely. The retrieved context becomes its
primary source of truth for answering your specific
question. Wow, okay. That really changes the
game. It's not... just drawing on its vast, but
maybe outdated, internal knowledge. It's using
a targeted data set you just handed it. So why
is this so important, especially for businesses?
The article listed some key benefits, right?
Oh, absolutely. It's transformative for several
reasons, particularly in an enterprise setting.
A huge one is accessing up -to -date information,
pulling from external sources that are current.
Makes sense. What else? Another critical one
is using private data. Companies can finally
leverage their own internal reports, customer
data, internal wikis, whatever, to ground the
AI's responses. That's massive for building tools
that are actually useful within that specific
company. And I guess that directly tackles the
hallucination problem we talked about. Exactly.
That's a core benefit the article highlights.
A significant reduction in hallucinations and
much better factual accuracy. Because the LLM
is told, often very explicitly, answer only using
this context. It's less likely to just make stuff
up. Right. It's answering based on verifiable
sources you provided. The article also mentions
just generally enhanced response quality and
relevance. If the AI is grounded in specific,
accurate data, the output is naturally going
to be better, more diverse, more customized for
what the user actually needs. And using verifiable
sources. That sounds like it improves safety,
too. Definitely. AI safety is another benefit
mentioned. Relying on retrieved sources, which
you could potentially trace or cite, builds trust.
And then there's flexibility. RG lets LLMs tackle
really complex questions that need information
for massive amounts of text, internal or external,
far more than could ever fit in a standard prompt.
This sounds incredibly powerful, like turning
a generalist AI into a specialist on demand.
But, uh... building this. It can't be trivial,
right? The article went into the components and
steps needed to actually implement RG. No, you're
right. It's definitely an engineering effort.
It requires putting several key pieces together.
It really starts with data ingestion and preparation.
You've got to get the data from wherever it lives,
APIs, files, databases. All the messy enterprise
data sources. Exactly. And that often means a
lot of work cleaning it, transforming it, maybe
structuring it, especially company data. So it's
actually usable by the RG system. Robust data
pipelines are pretty key here. And you mentioned
earlier that LLMs can only handle so much text
at once. The context window limit. Right. Which
leads directly to the next step. Chunking. You
have to break down those potentially long documents
into smaller, manageable pieces. Chunks. How
do you decide how big the chunks should be? Well,
the source discusses different ways. You could
do fixed -length chunks, or maybe use NLP libraries
like NLTK or Spacey, the article mentions, to
be smarter about it. Like breaking its sentence
or paragraph boundaries. What works best depends
on the text itself, the kinds of questions people
will ask, and definitely the context window size
of the LLM you plan to use. Oh, and using some
overlap between chunks is often a good idea,
the source notes, to make sure you don't lose
important context right at the chunk boundary.
Okay, break it into overlapping pieces. Then
you need a fast way to find the right piece when
a question comes in. How's that done? That's
where embeddings come in. Each of those text
chunks gets converted into a numerical vector
and embedding. You use a special model for this,
an embedding model. The article mentions ones
like text embedding at a 002 or 003. And these
embeddings capture the meaning. Exactly. They
capture the semantic meaning. So chunks that
talk about similar things will have vectors that
are mathematically close in this high dimensional
space. Fascinating. And where do you store all
these chunks and their vector embeddings? They
go into specialized databases called vector databases.
The article gives examples like Redis, Azure
AI Search, Pinecone, Weaviate. These databases
are built specifically for handling vectors efficiently.
They create a vector index. An index for vectors.
Yeah, which lets you do incredibly fast similarity
searches, finding vectors that are close to a
target vector, like our query vector. The source
also points out that Some platforms like Azure
AI Search can do hybrid search, combining that
vector similarity with traditional keyword search
can be really effective. OK, so data ingested,
cleaned, chunked, embedded, and indexed in a
vector db. Now I ask my question, walk me through
the search. Right, so your question, your query,
also gets converted into a vector using the exact
same embedding model that was used for the chunks.
Consistency is key. Absolutely. Then, this query
vector is used to search against the vector index
in the database. The database very quickly finds
and returns the chunks whose stored vectors are
most similar, most close, to your query vector.
Those are the most semantically relevant bits
of info from your entire knowledge race for that
specific question. And those are the chunks that
get sent to the LLM? Almost. There's one more
critical step before the LLM sees anything. Prompt
formulation. You have to dynamically build the
prompt that goes to the LLM. Build the prompt?
Yeah, you take the user's original query and
you insert the relevant chunks you just retrieved.
This whole package query plus context is put
together, often using a specific API format like
the Chat Completion API. And importantly, you
include instructions for the LLM. Like use only
this information. Exactly that kind of instruction.
Answer the question based only on the provided
context. And there's a technical constraint here
the article mentions, managing the token budget.
You have to make sure that query plus all the
retrieved chunks don't exceed the LLM's maximum
input size. It's context window limit. Right.
Can't send it too much stuff. And then, finally...
Then, that carefully constructed augmented prompt
is passed to the LLM API. The LLM takes it all
in and generates the final answer in natural
language, using the query for direction and,
crucially, relying on that specific retrieved
context as its factual foundation. Wow. Okay,
so there's quite a pipeline there. Ingestion,
chunking, embedding, indexing, retrieval, prompt
construction, all before the LLM even generates
a word. But just getting it working technically
isn't the end of the story for a business, is
it? The article mentioned deployment considerations
for actually using this in an enterprise. Absolutely.
Getting RAG into production reliably involves
thinking about several other factors. Scalability
is huge. The system needs to handle potentially
lots of users and the data keeps growing. Vector
databases help here. They're often designed to
scale. Performance must be key too. People won't
wait forever for an answer. Definitely. Performance
and latency. You need to track things like token
usage, how long it takes to get a response back,
which leads to observability. You need good monitoring
and logging. The article mentions tools like
MLflow or Prometheus, ways to see what's happening,
track performance, and debug when things go wrong.
And it has to play nice with existing systems,
I imagine. Exactly. Integration. Yeah. How does
this RRag system fit into the company's existing
workflows and applications? And finally, maybe
most importantly for enterprise, security and
governance. Well, you need robust security. You
need to manage data access, making sure users
only retrieve information they're actually allowed
to see based on their permissions. And of course,
ensuring everything complies with relevant industry
regulations or data privacy laws. So when you
step back, the article really paints RAG as this
foundational technique. It fundamentally changes
how we can use these powerful LLMs. Yeah, it
seems like it transforms them from being these
like... general knowledge boxes with static info
into dynamic tools that can actually interact
with specific current even private company data.
That's exactly it. It cleverly combines finding
the right information efficiently, the retrieval
part, with the amazing text generation abilities
of LLMs. And by doing that, ARRAG really empowers
organizations to build AI applications that are
much more accurate, much more relevant to their
specific needs, and safer because they're grounded
in verifiable information. They can be tailored
to a company's unique knowledge landscape. That
really was a deep dive into RRag, breaking down
the what, the why, and the how based on that
source material. Really helpful. Thank you. My
pleasure. And maybe it's something for you, the
listener, to think about. Considering how important
it is to ground AI in specific verifiable knowledge,
how might this idea of retrieval augmented generation
start to reshape the way you think about finding,
trusting, and using information in your own work
or your studies, or maybe even just in everyday
life?