Production RAG with LangChain & Vector Databases – Full Course

Master the transition from simple

prototypes to production-grade rag

systems by addressing the critical

scaling, debugging, and security

challenges that standard tutorials often

ignore. This comprehensive course covers

the entire rag pipeline from vector

database optimization and observability

to advanced agentic and multimodal

architectures. You'll learn to make sure

your AI applications are robust, secure,

and ready for deployment.

>> So, you follow a rag tutorial. It worked

on 10 documents, but then you decide to

add 10,000 documents and everything

broke. Sound familiar? Now, here's the

thing. 90% of rack systems out there,

they fail in production. And they all

fail for the same reasons. So, in this

course, we're not just going to build a

rack system. I know most of you have

built a lot of rack system that actually

don't work. But anyway, we're not just

going to do that. We're going to do

something different. We're going to

debug it. We're going to optimize it and

scale it for production. Here's what

we're going to be covering. First part

is building the foundation. So, we're

going to go over documents, chunks,

embeddings, and vector stores. The

second part, it's all about the five

main failure modes when it comes to rack

systems. So, why rag breaks and how to

fix each one of them. Parts three and

four, we're going to optimize what we've

done. We're going to optimize for

quality, then scale for production. Part

five, we're going to build a complete

rag system. And part six, this is where

we're going to look at the current

cutting edge [clears throat] stack. So,

we're going to look at agentic rag,

we're going to look at graph rag, we're

going to look at contextual retrieval,

and of course, multi-model rag. Here's

my promise. By the end, you will have a

production ready rag that actually

works. I mean, assuming that you're

going to finish this course, but that's

on you. All right, let's build. Let's

get started with a full rag overview.

The idea we have the user query. So,

this is the input question comes in and

of course stripping down all the

intricacies here. Uh the idea is that we

want to get information from database.

So we want to find relevant documents

through a retriever and then the query

as well as the context from the

retriever combined with the prompt

that's what's taken to the large

language model to generate the answer.

So then we have response which is

grounded in the documents relevant

documents that is okay. So all of that

of course is driven

by the vector store. So in vector store

we have embedded documents and we have

indexed which are indexed for search.

The key concept to keep in mind is that

rag grounds large language model

responses in actual documents which

reduces hallucination which means then

the large language model is not going

crazy and is not going to go and

completely hallucinate our responses. So

here's a basic rag chain. So we have the

question, we have the context. This will

come in parallel inputs of course. And

then we chain that with the prompt

template. In this case, we pass all of

that into large language model. And then

if need be, you most cases we need this.

We use an output parser, right? To sort

of parse our output and also create

parser. So we can control and customize

our output. Context and question are

going to go hand in hand. This is part

of augmented our retrieval augmented

generation side of things. So it's

important to know that context comes

from the retriever. And so the retriever

is important because it goes and gets

the relevant documents but also all has

to be driven by the question the query.

And in this case the question is going

to go through what we call the runnable

pass through. If you remember, it's just

a way of saying, okay, do not modify

anything when it comes to the question.

It has to be as original as possible and

just pass through as we pass through

this parallel input processing. Okay, so

question passes through unchanged. So in

our prompt template, we want to make

sure that we specify, we ground the

large language model with the

information and instructions it need. So

we may say something hey make sure to

answer based only on the context which

was retrieved from the retriever and the

question which we retrieved from the p

which we got from the user but we made

sure to pass through unchanged. So now

we have a prompt template that has the

context which is part of the retriever

and all that stuff and the question

which is needed so that the large

language model is able to generate the

response. It's important that the large

language model as it generates the

response with all the documents with all

the retrieved relevant documents that it

also knows how to say I don't know and

so this is when we pass instructions for

instance without instructions if we ask

what is quantum computing it will just

go ahead and say quantum computing uses

cubits and starts making stuff up okay

now with instruction what is quantum

computing it will say well I don't No, I

don't have enough information pertaining

to this topic. This is way better than

this for obvious reason because we don't

want hallucination. Even though it may

sound coherent, but it is hallucinating,

which means it's just making stuff up.

You will see that the prompt is also

very important for many reasons. One of

which is we are able to quickly ground

our large language model. So the prompt

pattern that you should look into is

something like this. You would say

answer based only on the following

context. So you can also say if the

context doesn't contain the answer say I

don't have information about that or I

don't know or something along those

lines. Okay. Of course in this prompt

passing the context and the question

along. This is the lowhanging fruit when

it comes to prompt engineering. Yet a

lot of people forget about it or don't

pay attention to this. Prompting is

extremely important. But it doesn't have

to be such a large thing to be done, but

it can be something that we can use a

pattern especially when we don't want

the LLM to elucinate answers. So make

sure to specify in the prompt pattern

that only follow the following only to

answer based only on the following

context and then if it doesn't know just

say I don't know and you pass the

context and of course the question and

to make sure that the results that are

coming from a rack system are indeed

based off or grounded in the documents

retrieved documents we want to make sure

that we also add some sources. Okay. So

the retriever would output something

like this page content here and then you

will see something like this source

doc.pdf. This is very important not only

for the users but also for the system

itself. So it knows okay I got this

answer but I got it from where? I got it

based on this document based on this

header based on this place. So this is

real good to always allow to always

retrieve the actual sources as well. And

it's really easy to do that as you will

see because we can just call the format

docs with source which is going to add

source tags to each chunk. That way when

we are retrieving using a retriever it's

going to be able to pull the correct

chunk with the actual resources. Okay.

And so we'll have something formatted

context such as this source doc PDF and

then page content and all of that. This

is how you do rag with sources. The

reason why sources matter is because

users are going to be able to verify

those answers. So we can have citations

and it's going to obviously build trust.

So our system is going to build trust

and our customers or users are going to

be able to say okay I know the answer is