So this is really interesting. This

company Sarah just built a knowledge

base that I think is not total

And that's hard for me to say because up

until now, virtually every instance of

knowledge bases or second brains or

whatever have just been total hot air.

This is a massive AI hardware company

that essentially creates super quick

inference chips and they're used by a

variety of major billion dollar

companies that you guys probably have

heard of. And at no point in this big

tutorial which they just wrote a big

blog post on did they show an obsidian

graph. At no point did they show a

floating 3D brain in a tube. Uh you know

instead what they showed is they showed

a very reliable and robust data

ingestion pipeline that allows virtually

anybody in their company to answer any

question that they have about the

business and about things that other

people have done in the business. What

they've done is they've fed in like

gigabytes of text and image data from

platforms like Slack. Every Slack

conversation that's ever occurred in the

organization. Um their wiki and

Confluence, all of their code repos and

GitHub, you know, their net list PRM

docs as well as custom databases.

They've just slashed or rather I should

say squashed all of this data into

embedding space which you can just think

of as concepts that an AI could query

over. So that if somebody said, "Hey,

what was that thing Paul was talking

about 3 days ago?" ago, it had something

to do with, you know, embeddings. It can

actually go in to the Slack without

having to, you know, query the API or

whatever and just always know what's

going on in the business. So, what I

want to do in this video that isn't

clear is I want to show you what this is

and then I actually want to build one

alongside you. I've already built one of

these for my own business and it's quite

useful already and that's just as a

small team. I can imagine how if you

have thousands of people in your

organization, stuff like this starts to

scale and get really, really good. Um,

and this is also just as mentioned like

one of the ones that's not

There's a lot of noise on YouTube

obviously because everybody's trying to

push their own particular way of doing

things. This is not my way of doing

things. This is this major company's way

of doing things um that I happen to

think is actually quite practical and it

it is not all about visualizing

something goofy for people that are non

super technical. Now to really

understand this system you first need to

understand what retrieval augmented

generation is. Also commonly referred to

as rag. Rag is the cornerstone behind

more or less all of this stuff. So, it's

worth at least a brief refresh if this

is the first time that you're

encountering this or not. What rag

basically does is it allows models to

answer questions specifically about a

set of data as opposed to just in the

general sense over all data. Like for

instance, if I were to type how tall is

Nick into my AI model chat box right

now, it would probably ask me who are

you talking about? Who is Nick? Well,

that depends broadly on which Nick

you're talking about. if you meant, you

know, Nick Jonas, Nick Jonas is XYZ

tall, right? That's a very general

question. So, it's likely that you're

going to have a general answer. And the

reason why is because it's enumerating

over all of its data. So, just to

visualize this and because I thought

it'd be kind of funny to make the whole

world know I'm taller than Nick Jonas.

Um, you know, here's a quick

visualization of maybe all the possible

answers you could get. You know, how

tall is Nick? Which Nick? Nick Jonas is

5' 10. That depends, right? Um, now the

question is, imagine hypothetically if I

didn't just say, "How tall is Nick?"

Imagine if the prompt that I fed into

this model was Nick is 6'2

and then I added some lines over here

and then I said how tall is Nick. What

do you think the model would say? Well,

it's pretty obvious how tall Nick is,

right? We just fed in a piece of

information immediately before the rest

of the prompt that just says how tall is

Nick? So, what do you think the model's

going to say? Well, the model's probably

just going to say, well, Nick is 6'2. It

doesn't even know really which Nick I'm

referring to or anything like that

because it just has the information

directly in its prompt, right? And so

what we've done is we basically

collapsed all of the possible responses,

all the variability of the model and

made it hyperspecific. We basically made

it like exactly what we want every time.

Okay? So all rag really is retrieval

augmented generation is it is augmenting

the AI generation of a piece of text by

retrieving information like Nick is 6'2

from some database. Okay. Now the key is

it's not just a database. It's not like

we just hypothetically have height of

every man on planet earth and then

somehow we store the right entry in.

What it usually is is it it sort of

exists in a language that LLM and large

language models can intuitively

understand called the embedding space.

You should know that all retrieval

augmented generation really is is just a

system that allows us to store

information before the question. Now,

hypothetically, let's say you were

trying to build a really cool knowledge

base that just answered all the

questions inherent in your business,

everything that's gone on in the last 24

hours. Um, well, you can apply that same

approach. Like if I just asked my little

knowledge base, hey, what did Peter say

at that talk last week? It's probably

going to go and collapse into or rather

spread a variety of different answers

and be like, who the hell's Peter? What

talk are you talking about? I don't even

know what year it is, man. I'm just a a

text corpus. So, what we need to do is

we need to find a way to insert

information. So, you know,

realistically, what we want is like uh

Peter Jackson delivered a talk last week

at 6:52 p.m. on Thursday. in the talk he

discussed X Y and Z. What we want is we

basically just want a way to prepen that

uh before the prompts, right? So all

this knowledgebased system from Sarah

does is it just allows you to basically

retrieve a relevant piece of information

from your company and then inject it

above your prompt such that if this is

your main prompt over here, this is just

like your sort of injected and I don't

know, I'm just going to call it like

help. I mean, the model doesn't know the

difference, right? the model just sees

the text whether it saw this or whether

it saw all of this combined. You know,

it's going to heavily change what the

model says, but it doesn't know that

like, you know, we've helped it out by

providing the information. It just

thinks like this is part of its training

data essentially. Um, and so that's

that's more or less what the system

does, and that's why it's so powerful.

It allows anybody in the whole company

to answer any question about anything

that's gone on. It also allows you to

store things like, hey, you know, here's

a set of problems and then here's a set

of solutions that we use to solve those

problems. So that if a newbie comes on

board, they're I don't know trying to

make some code fix or something and it's

not entirely working, they can just go

to the knowledge base and say, "Hey,

like why isn't this thing working?" And

they would say, "Ah, you're probably not

pushing to prod as we do in this

business. It's a little bit different

than most other businesses. Let me show

you guys how it works." All right, so

the first thing to know is this is made

by three handsome gentlemen, Isaac,

Daniel, and Mike. At least they're the

ones that wrote the the blog post. Uh

and currently they're being asked

something like 15,000 questions every

day. Now, this is obviously a pretty

long blog post. There a bunch of really

cool visualizations and stuff, but for

the sake of your time, um, let me find

the most important sections of this so

that you have everything you need. Okay,

the first is the anatomy of the

knowledge base. And so this knowledge

base is composed of three major

sections. The first is it's a platform

for collecting and storing internal

data. And so that makes sense, right? We

need to find a way to basically get

knowledge from outside of, you know, the

system into the system. So the knowledge

inherent in conversations let's say of

people in the Slack the knowledge

inherent in the SOPs of the company the

the checklists the processes all of the

emails all of that stuff we basically

need a way to take all of this in the

ether and then stick it into this

knowledge base in an appropriate manner

so that it doesn't get super crappy. The

second thing we need is we need a

platform for querying that data. And so

now that we have the data in the the

brain so to speak the knowledge base we

need a way to extract it and kind of

pull it out. And so this for instance

would be if I asked my knowledge base a

question about hey grab stats on the

last week of performance for XYZ

software platform and then apply that

cool lens that Nick talked about in his

most recent uh company seminar to it.

That would be an example of you know

using this platform which we've

collected and stored internal data into

uh to query it and then pull something

out. And then finally, because Cabris

is, you know, a big company and they're

dealing in hardware, they also need a

layer that enforces authentication and

authorization with auditing, analytics,

see who's like accessing the data, why,

and so on and so forth. Now, I'm pretty

small relative to this massive hardware

company that works with billion-dollar

businesses all over the world. So, I'm

not going to do that. And really, the

thing that's most important for us is

just going to be one and two. And I'm

going to assume anybody in my business

is probably okay. So, let's actually

visualize what this looks like.

Basically, at the very top end, we have

a bunch of different data sources that

they're constantly ingesting, right, for

that first section to to to store a

bunch of data in their platform. And the

first is Slack. So, as I'm sure you can

imagine, any major business will have

some sort of messaging platform these

days. And basically what this does is it

scans through all of the messages that

are sent in all of the accessible

workspaces and then converts them into

what are called embeddings. And

embeddings are just another way of

referencing or referring to that data.

It's just uh it's a little easier for

models and LLMs to understand. And so

you can imagine, you know, if you guys

use Discord, you do this on Discord. If

you guys use email, then you wouldn't do

Slack. You just do it over email. But

the idea is you just need a place where

people are talking. The second is, you

know, most companies at least sizable

businesses will have some form of wiki.

So whether it's wiki, whether it's

Confluence, whatever it is, there's some

place that you guys have already tried

to sort of store all of your

information. And typically that's done

naively through like checklists and SOPs

and onboarding pages and welcome to

Clairvo. You know this company is going

to walk you through blah blah blah blah.

And so just like Slack, you sort of

stick that into this embedding space.

Next up are code repos. This is

particularly important if you're a

software business or software adjacent.

Now in my case I consider my business

software or software adjacent. So you

know scanning through all the code repos

is going to be important. Net lists, PRM

docs, you know this stuff is going to be

more uh relevant again to software

businesses. And then also any sort of

custom databases you have that store

information. And so maybe you are doing

some sort of sales or rather

spearheading a sales department I should

say and you guys have databases that

have info on all of your KPIs. So you

know uh how many sales are being made,

how often you know what we can do is we

can actually take all of that data that

formatted database information and then

just store it as these embeddings. Okay.

And that sort of takes me to that idea

of like what is an embedding? Well an

embedding basically just takes the raw

data itself. So let's say hypothetically

um the data that we want to stick in

this pipeline is just a I don't know

it's it's Nick thinks

I don't know penguins are cool. So what

we have is we have this naive sort of

text data here and then what we'll do is

we'll actually pass that through a large

language model. So this might be I don't

know man like chat GPT claude it could

be an open source model it could be

anything. And then we just ask it

questions about this and we actually say

hey who's Nick? Why did Nick say that?

What time did Nick say that? Where did

Nick say that? when did Nick say that?

And so on and so forth. And so at the

end of it, we don't just have this naive

piece of text that says Nick thinks

penguins are cool. We have Nick thinks

penguins are cool, you know, time 5:32

p.m. We have, I don't know, subject Nick

Sarafh. We have description, you know,

and then here maybe it would say

something like, you know, oh, Nick is

the founder of XYZ organization. Nick

does blah blah blah and blah blah blah.

So what we're doing is we're basically

taking a core piece of data which you

can visualize sort of like I don't know

just this cube over here and then we're

adding metadata to it. We're adding a

bunch of additional kind of outcrop

pieces of information. So it's no longer

just that tiny little nugget in the

middle. Now it's like okay now we know

the time of date it was sent. We know

who it was sent to, where it was sent

from, who the person is, why any of this

is relevant, what the point is that Nick

was trying to make, and so on and so

forth. And I mean it's pretty similar to

I don't know like the way that a camera

works if you think about it. When you

take a photo with a digital camera,

right? You have the photo which is the

core data, but then you also have a

bunch of metadata around that. You have

stuff like, you know, what time it was

taken, what the color scheme of the

photo was, where, you know, a lot of

these cameras are now storing GPS info,

which is kind of terrifying. But anyway,

what I'm trying to say is we're not just

storing the data itself, okay? What

we're doing is we're adding a bunch of

stuff to that data and then storing it

in a way that the machine understands.

And the reason why that's important is

because much like your brain remembers

new things more better and stronger,

this system remembers new additions to

the knowledge base more, better, and

stronger. And so we have the source, we

have the time stamps, we actually know

how to weight the data. If the data

comes from some super low-level employee

3 years ago, we're going to weight it a

lot less strongly than if the data comes

from Nick, the founder of the business,

and it happened 30 seconds ago. You

know, if somebody asks for information

related to some recent seminar or

something like that, it will go through

not just all seminars equally like most

naive language models would do. It'll go

through the most recent seminar uh sort

of preferentially. It'll be like, okay,

recency. What does that mean? Okay,

what's the date? Okay, cool. And then in

this way, you're going to have a bunch

of these like quote unquote memories of

this brain um that are sort of uh uh

ranked based off the date, based off the

time, and based off the relevance. Okay,

so that's that. And then the third step

is obviously actually quering that data.

And so that data is queried you know via

MCP you know web UI AI agents uh people

are sending messages into a chat box

whatever it is you know what what occurs

really is it goes in reverse the query

goes through the embeddings the

embeddings represent a particular form

of information say a slack message okay

and then you know we what we do is we

pull the embedding version of that uh in

order to get the answer key practical

example of this with a slack message and

you know I think the examples I provided

here do kind of suck so bear with me But

there'll be a message maybe sent in the

CKPT- support thread. Okay. And at 9:14,

Amaya will say, "Hey, the restore stalls

after manifest load on the larger

cluster, but small runs are fine." Maybe

Owen then says, "Yeah, I can reproduce

this with 128 hours." The log stop

before the cache warm-up, though.

Basically, what happens is question

summary, right? Maybe here's a

resolution a few minutes later. We take

all of the information in a chat thread

sort of divided into these windows and

it'll all be done for you automatically.

And then it'll be essentially created as

a structured artifact which you guys

can't really see here. Let me zoom in.

And so this is the metadata that I was

talking about because we'll have a large

language model um take this information

and ask questions about it. You have

things like question, hey, why does the

restore stall after the manifest load?

Well, guess what? The summary of the

sort of this conversation is well large

restores stop before the cache warm-up.

you know, if I knew what the hell this

meant, it might be a little bit easier.

But maybe it's like, hey, uh, who was

that guy that came in to do that talk on

Thursday? And maybe the summary is now,

um, you know, the guy that came in to do

the talk on Thursday was Nick. You maybe

there's a resolution step if it's

something that needed to be resolved.

And so in this case, you can imagine how

now we have the ability to actually, um,

I don't know, like literally store the

answer to this question permanently

inside of the company, meaning the

company grows more efficient and more

effective over time. You then store a

bunch of things like maybe code rest

maybe that just means I don't know like

what is the slack thread where all that

information is stored and maybe systems

that includes a bunch of systems that

these people are working on. It'll

actually store a list of them. Obviously

it includes other information as well.

It includes like the source ID. It'll

include um I don't know the time and and

and date and so on and so forth. But

yeah, I mean like that is that's about

how detailed we're getting under the

hood. Uh but we again don't need to

store any of that stuff. Just wanted to

make sure you guys all understood what

was really going on under the hood. So

you don't need to know everything that I

just said. That's okay. I'm going to

walk you guys through the actual build

process. And the good news is I don't

even know, you know, what 90% of this

stuff really is. I just understand it at

a high level, which is sufficient. Um,

as long as you understand the way that

the data flows, we can actually take

that and use that to build our knowledge

base. You don't need a crazy dev to do

this. Um, you know, we can 100% do this

ourselves. Okay. So, how do you actually

build something like this in practice?

Well, the very first thing you need to

do, so you need to whip up a coding

agent of some kind. And so, in my case,

I'm using Claude Code V2.1.211.

I'm not saying you have to use Claude

Code. You can use one of many available

open models, but this Fable 5

intelligence right now just happens to

be the best, aka the most performant,

and um I have the money to to pay for

it, so might as well. Okay, so you can

get whatever model you want, Claude

Code, you know, Chat GPT, Codeex,

whatnot. As long as it's like a coding

model, it can actually make changes on

your computer, you're good. And if you

want more information on that, then just

um you know, check out my my 4-hour

Cloud Code course. So, all we really

need to do to make this work is I'm just

going to copy this whole blog post in.

Okay? Okay. And then I'm going to go all

the way back to my Ghost TTY terminal.

That's just what I happen to be using

for this. Then I'm going to paste this

in. Then I'll say, I'd like you to build

something like this for, and in my case,

I'll say Nick Sarif Media LLC. I have a

Slack, a set of emails, an email

address, a GitHub, and a YouTube

channel. I want us to build ingestion

pipelines, which is just how we get data

into the system for all of them. I'll

also say PS this is a demo. I've already

done this. Given that it's a demo in

front of the audience, I want you to

build it totally fresh. The reason for

that is because I've just already done

this several times. And what I wanted to

do is just pretend that it's totally

new. Now, the cool thing is, as

mentioned, you do not need to know how

this works. You can just go through

this, okay, entirely autonomously. Uh

the model can sort of figure it out. Um,

if for whatever reason Cabris has, I

don't know, put a block on their page,

then rather than feeding the URL, you

can also just go back to Google Chrome,

copy the whole thing manually, and

literally just paste it in. Um, the

important thing is you just need to get

it in to like this intelligent model.

Now, what it'll do next is it's going to

attempt to log in to all of the various

accounts of reference. So, in my case, I

actually have accounts that are already

logged in. For instance, I have GitHub

logged in. I have my YouTube channel

logged in. I have my email addresses

logged in. And so it'll do so almost

entirely automatically basically at

once. But I want you guys to know that

that might not necessarily be the case

for you. And so if you guys wanted for

instance an email to be set up, all you

have to do is just open up a new

instance of this. And I'm just going to

go claw-bose. I'm just going to zoom in

a little bit so you guys could see both

of these. And then I'll say something

like, "Hey, I want you to get access to

my email." Then I'm just going to give

it one of my emails, nickleclick.ai.

You should be able to query each email,

read all of my emails, send emails,

basically have full admin access. How

would you do this hypothetically if you

didn't already have access to it? Now,

I'm asking that question because it

already has access to my emails and I

don't want to sit here for 4 hours

showing you guys it say I already have

access to your email. And as you can

see, it eventually will tell you here's

how you'd set it up from scratch. You

would have to open up a Google Cloud

project, enable the Gmail API, do an

OOTH consent screen to create an OOTH

client, some sort of desktop app. Uh you

would have to do a one-time consent

form. You'd also need some sort of

refresh token. I guess what I'm trying

to say is it's not it's not very

difficult. This would realistically take

you about 5 to 10 minutes for every

pipeline of information that you want to

set up. And so that's the cool thing

about agents. I mean, you can just ask

them, hey, how do you do this? And if

the model is smart enough, in our case,

Opus 4.8 8 in this instance, but you

know, you could use Fable 5, you could

use GPT 5.6, so you can use whatever you

want. Um, you'll get the credentials

that you need in order to build set

injection pipeline. And on the left hand

side, speaking of it is currently doing

the building. Now, because I kind of

want to see what's going on, I'm

actually just going to go /btw. That's a

cloud code specific feature. And I can

say, visualize the system you're

building for me in ASI. In case you guys

didn't know, ASKI is just a text way to

demonstrate sort of visually like what a

di like a diagram or something like

that. And so what this is now doing is

while the main thread is running, I have

another thread that's going on on top of

that just asking it various questions.

And so I'm saying visualize the systems

you're building and asking. And here's

what it's doing. It's actually showing

me it so we could see. I'm just going to

zoom out. And so what we're doing is

we're basically building that exact

thing. We have a Slack over here. We

have three accounts over Gmail. We have

my GitHub. Then we also have my YouTube

channel. And so these are the

connectors. So this is sort of like the

APIs and uh MCPs and whatever it is that

allows us to pump stuff in there. We

have the distillation step like I talked

about earlier where we're basically

taking each thread passing it through a

ha coup in this case feeding in a

question a summary resolution systems

and then we're ingesting this into our

actual pipeline or database if we're

giving it just one table in what looks

like postgrql. And then down at the very

bottom here we have retrieval setup with

full text where we look for exact

matching tokens and stuff like that and

then embedding. Okay. And right after

this is all done, I'm just going to jump

to it and then I'll show you guys how it

actually works when you query it. And

now you can see sort of a artifact that

shows the same model asked the same 20

questions, one with the knowledge base

and one without. You can see that the

one that had the knowledge base answered

17 out of 20 questions correctly. Um the

other three were just honest partials or

declines. It just said, "Hey, I don't

really know what's going on cuz I don't

have the evidence to to back it up."

Whereas without the knowledge base, I

answered zero. So I mean, these are

really high quality. Um, comparatively,

you can imagine how rolling this out

inside of any companies would

significantly improve both the quality

and then the speed with which you get

things answered. So, for instance, which

AI company recently pitched a paid

sponsorship for the Stacked podcast? In

case you guys didn't know, I run a

podcast with Jack Roberts called

Stacked. And, you know, we're we've

recently grown a little bit. Um, I think

we're up to 5,600 subscribers now. So,

Jack and I kind of screwing around,

having a fun time. We're almost at 6K.

Well, uh, you know, if you just asked

that question natively, obviously you

wouldn't get the answer. But in our

case, we know it's abacus.ai. They

pitched a paid sponsorship for the

stacked podcast promoting as chat LLM

all-in-one AI super assistant. The

outreach came from insert person here.

Did we take it? Absolutely not. Or how

about this one? Which GitHub repos did

NSM push to most recently? We actually

have the exact GitHub activity right

over here. And it was just me replacing

sort of a defunct company name with a

new company name. Now, this isn't super

impressive. any sort of rag could do

this. What is impressive is the fact

that this data is now being ingested

completely automatically and I have

nothing that I need to do with it. It

occurs entirely without my control not

without my control but not requiring my

management. Um this will occur in the

background and so I can ask this any

sort of question that I want at any

point in time. Unfortunately, the thing

you can't really visualize is just how

much better this is than naive rag,

which most other people are doing

because we have information like the

date and time, aka how long it's been

since the thing was created. We also

have significantly higher quality rag.

You know, I only had 640ish documents

inside of my system. A company like

Cerebrus probably has, you know,

hundreds of thousands. Uh the thing that

differentiates like a crappy

knowledgebased uh system that you ask

questions to and have woven into your

business with the ones that are good

isn't necessarily how many documents you

have, but it's how effectively can you

retrieve the highest quality ones, the

most relevant ones. And uh that's the

question that this sort of rag system

answers. So I'm not going to tell you

have to use this one. There are

obviously a bunch of different rags that

you can use and there also a bunch of

different approaches to knowledge bases,

but this is done by a major business.

These guys aren't screwing around.

They're not going to do something like

this if it doesn't actually generate a

return on their time and energy. And as

it stands right now, this is definitely

generating a return on time and energy.

It's generating a return on time and

energy for me because I have this

syndicating and then embedding and then

pulling and and enriching all content

that I'm currently creating and that is

going into my business. It's it's a it's

an abundant training resource that

immediately gets everybody up to speed

in my team. uh you know, it's something

that I can just dole out literally with

the snap of a finger and I can give them

the same brain that I personally have

spent a long time creating. And the

unfortunate thing is doesn't look super

sexy. I don't have a big floating brain

in the middle of my screen, but you

know, it's the same resource that's

accessible via Obsidian or via, you

know, Graphify or whatever the heck. Um,

this is just probably the higher ROI way

of doing it because of the frequency

waiting, the time waiting, and stuff

like that. Anyway, as you guys could

tell, I'm uh singing hard for Cerus. a

dope company and this is not a sponsored

post in any way, shape or form. I was

not paid any money to do this. I just

think it's probably the coolest

knowledgebased implementation that I've

ever seen. So yeah, that's that. If you

guys like this sort of thing, definitely

check out Maker School. It's my

day-by-day accountability roadmap where

I show you guys how to acquire your very

first customer for an AI or automation

service, which could be building rag or

knowledgebased systems like this um for

money within 90 days, or you get all of

your money back. So I have a refund

guarantee because it works really well

and obviously I would love to see you

there. Have a lovely rest of the day.

Subscribe to the channel if you haven't

already.