Librarian Analogy Explains Retrieval-Augmented Generation
Key Points
- The journalist‑librarian analogy illustrates Retrieval‑Augmented Generation (RAG), where a language model (the journalist) relies on an expert data source (the librarian) to fetch relevant information.
- In business contexts, the “user” can be a person, bot, or application posing queries that combine general language understanding with domain‑specific data, such as “What was revenue in Q1 for customers in the Northeast?”
- Because detailed, time‑varying business facts aren’t encoded in a pre‑trained LLM, they must be retrieved from external sources like databases, PDFs, or other applications.
- A vector database stores both structured and unstructured data as embeddings—mathematical vector representations—that are efficiently searchable by similarity.
- The RAG workflow queries the vector store for relevant embeddings, feeds that retrieved context to the LLM, and then generates an answer that combines the model’s reasoning with up‑to‑date, domain‑specific information.
Full Transcript
# Librarian Analogy Explains Retrieval-Augmented Generation **Source:** [https://www.youtube.com/watch?v=qppV3n3YlF8](https://www.youtube.com/watch?v=qppV3n3YlF8) **Duration:** 00:07:53 ## Summary - The journalist‑librarian analogy illustrates Retrieval‑Augmented Generation (RAG), where a language model (the journalist) relies on an expert data source (the librarian) to fetch relevant information. - In business contexts, the “user” can be a person, bot, or application posing queries that combine general language understanding with domain‑specific data, such as “What was revenue in Q1 for customers in the Northeast?” - Because detailed, time‑varying business facts aren’t encoded in a pre‑trained LLM, they must be retrieved from external sources like databases, PDFs, or other applications. - A vector database stores both structured and unstructured data as embeddings—mathematical vector representations—that are efficiently searchable by similarity. - The RAG workflow queries the vector store for relevant embeddings, feeds that retrieved context to the LLM, and then generates an answer that combines the model’s reasoning with up‑to‑date, domain‑specific information. ## Sections - [00:00:00](https://www.youtube.com/watch?v=qppV3n3YlF8&t=0s) **Journalist‑Librarian Analogy for RAG** - The speaker likens a journalist consulting a librarian for relevant books to Retrieval‑Augmented Generation, illustrating how a user queries a system that pulls information from a vector database to answer specific business questions. ## Full Transcript
so imagine you're a journalist and you
want to write an article on a specific
topic now you have a pretty good general
idea about this topic but you'd like to
do some more research so you go to your
local
library right now this
library has thousands of books on
multiple different topics but how do you
know as the journalist which books are
relevant for your topic well you go to
the librarian now the librarian is the
expert on what books contain which
information in the library so our
journalist queries the librarian to uh
retrieve uh books on certain topics and
the librarian uh produces those books
and provides them back to the journalist
now the librarian isn't the expert on
writing the article and the journalist
isn't the expert on finding the most
upto-date and relevant information
but with the combination of the two we
can get the job
done love this sounds like a lot like
the process of rag or retrieval
augmented generation where large
language models call on Vector databases
to provide key sources of data and
information to answer a question H I'm
not seeing the connection can you help
me understand a little bit better
sure so we have a
user in your scenario it's that
journalist
and they have a
question so what types of questions
would you want to ask right maybe we can
make this more of a business context
yeah so let's say this is a business
analyst and let's say they want to ask
um what was Revenue in q1 from customers
in the Northeast region right so that's
your
prompt okay so a couple questions on
that user does it have to be a person or
could it be something else too yeah so
this doesn't necessarily have to be a
user it could be a
bot or it could be another
application even the question that we're
talking about what was our Revenue in q1
from the Northeast you know the first
part of that question it's pretty easy
for you know a general llm to understand
right what was our Revenue but it's that
second part in q1 from customers in the
Northeast that's not something that lln
are trained on right it's very specific
to our business and it changes over time
so we have to treat those separately so
how do we how do we uh manage that part
of the request exactly you'll need
multiple different sources of data
potentially to answer a specific
question right whether that's maybe a
PDF or another business application or
maybe some some images whatever that
question is we need the appropriate data
in order to provide the answer back what
technology uh allows us to aggregate
that data uh and use it for our llm yeah
so we can take this data and we can put
it into what we call a vector
database a vector database is a
mathematical representation of
structured and unstructured data similar
to what we might see in an array gotcha
and and these arrays are uh better
suited or easier to understand for
machine learning or generative AI models
versus just that uh underlying
unstructured data exactly we query our
Vector database right and we get back an
embedding that uh includes uh the the
relevant data for which uh we're
prompting and then we includeed back
into the original prompt right yeah
exactly that feeds back into the prompt
and then once we're at this point we
move over to the other side of the
equation which is the large language
model gotcha so that that prompt that
includes the vector embeddings now are
fed into the large language model which
then produces the
output with the answer to our original
question with sourced upto-date and
accurate data exactly and that's a
crucial aspect of it as new data comes
into this Vector
database or things that are updated back
to your relevant question around
performance in q1 as new data comes in
those embeddings are updated ated so
when that question's asked a second time
we have more relevant data in order to
provide back to the llm who then
generates the output and the answer okay
very cool so Sean this sounds a lot like
my original analogy there with the
librarian and our journalist right so
the journalist trusts that the
information in the library is accurate
and correct now one of the challenges
that I see is when I'm talking to
Enterprise customers is they're
concerned about deploying this kind of
techn techology into customer facing
business critical applications so if
they're building applications taking
customer orders processing refunds
they're worried that uh uh these kinds
of Technologies can produce
hallucinations or inaccurate results
right or perpetuate some kind of bias
what are some things that uh can be done
to help mitigate some of these concerns
that brings up a great Point love right
data that comes in on this side but also
on this side is incredibly important
into the output that we get when we go
to make that prompt and get that answer
back so it really is true garbage in and
garbage out right so we need to make
sure we have good data that comes into
the vector database we need to make sure
that data is clean governed and managed
properly gotcha so what I'm hearing is
that things like
governance and data
management are of course crucial to the
vector database right so making sure
that the actual information that's
flowing through into the model such as
the business results in the sample
prompt we talked about is governed and
clean but also crucially on the large
language model side we need to make sure
that we're not using a large language
model that takes a blackbox approach
right so a model where you don't
actually know what is the underlying
data that went into training it right
you don't know if there's any
intellectual property in there you don't
know if there's inaccurate IES in there
or you don't know if there are pieces of
data that will end up perpetuating bias
in your output results right so as a
business and as as a business that's
trying to uh uh manage and uphold their
brain reputation it's absolutely
critical to make sure that we're taking
an approach that uh uh uses llms that
are transparent in how they were trained
and uh we can be 100% certain that there
aren't any
uh inaccuracies or data that's not
supposed to be in there to be in there
right yeah exactly it's incredibly
important especially as a brand that we
get the right answers we've seen the
results of impact and especially back to
our original question around what was
our Revenue in q1 right we don't want
that to be impacted by the results of a
question that comes from you know that
prompts one of our llms exactly exactly
so very powerful technology but it makes
me think back to the the library
uh our journalists and librarian they
both trust the data and the books that
are in the library we have to have that
same kind of confidence when we're
building out these types of gender AI
use cases for business as well exactly
love so governance AI but also data and
data management are incredibly important
to this process we need all three in
order to get the best result