---
title: 'What is RAG? Retrieval-Augmented Generation Explained'
source: 'https://youtube.com/watch?v=KNvkUH50xXM'
video_id: 'KNvkUH50xXM'
date: 2026-06-16
duration_sec: 0
---

# What is RAG? Retrieval-Augmented Generation Explained

> Source: [What is RAG? Retrieval-Augmented Generation Explained](https://youtube.com/watch?v=KNvkUH50xXM)

## Summary

RAG, or Retrieval-Augmented Generation, is a framework that overcomes the key limitations of standard language models by allowing them to retrieve and use external information before generating an answer. It works by first searching for relevant data from sources like internal databases or the web, then injecting that context into the model's prompt. This process drastically reduces hallucinations, ensures up-to-date answers, and allows access to proprietary data without requiring expensive model retraining.

### Key Points

- **Limitations of Base LLMs** [0:39] — Standard LLMs rely on static training data, leading to outdated knowledge, hallucinations, and inability to access private data.
- **What is RAG?** [1:11] — RAG (Retrieval-Augmented Generation) fixes shortcomings by first retrieving relevant information and then generating an answer grounded in that context.
- **Basic Flow of a RAG System** [1:51] — First, user asks a question. Then, the system searches its retrieval layer for relevant info. This retrieved context is assembled into the prompt, and only then does the LLM generate a final answer.
- **The Retrieval Layer in Detail** [2:27] — The retrieval layer can search internal sources (PDFs, knowledge bases) using vector search for meaning, and can also pull in external data like live web information via tools like web scrapers.
- **How Retrieved Data is Used** [3:14] — Retrieved data is injected as context before generation. The model's job remains reasoning and language, but it now works with up-to-date, specific information.
- **Benefits of RAG** [4:00] — RAG provides up-to-date answers, fewer hallucinations, access to private data, and more control. Crucially, it requires no model retraining; updating the source data is all that is needed.
- **RAG is Already in Use** [4:37] — RAG is not a futuristic idea; it's how modern AI tools like ChatGPT (web search), Claude (PDF upload), and Perplexity (citations) already operate.

## Transcript

For a long time after the initial AI boom, 
most chatbots were great for general questions,  
but always fell short on current 
events or developments. However,  
that has changed completely 
with the introduction of RAG.
And here’s the kicker: if you’ve 
used ChatGPT, Gemini, Claude,  
or Perplexity recently, you were probably 
already using it without even knowing.
I’m Mantvydas from Oxylabs, and in 
this video, I’ll explain what RAG is,  
how it works, and why it makes today’s AI 
systems much more reliable in real-world use.
Before diving into definitions, we 
need to start with a simple problem.
On their own, large language models don’t actually 
“look things up". They generate answers based on  
patterns learned during training. This creates 
three big limitations: their knowledge can be  
outdated, they can hallucinate when unsure, 
and they can’t see any proprietary data.
So if you ask a base model a question it 
doesn’t know, it won’t say “I don’t know”,  
but it will try to guess. RAG exists to fix 
this shortcoming. Not by changing the model,  
but by changing what the 
model sees before it answers.
So what exactly is RAG? This acronym 
stands for Retrieval-Augmented Generation. 
Retrieval is the keyword here. Instead of 
asking a language model to answer only from  
its training data, you first “retrieve” 
relevant information – such as documents,  
files, or web data – and then let the 
model “generate” an answer with all that  
context in front of it. In other words, 
RAG means “Find first, then generate”.
Now, to get a better feel of how that works, 
let’s look at a basic flow of a RAG system.  
This is the core idea, and it stays pretty 
much the same in almost every implementation.
First, the user asks a question. Next, the system 
searches for relevant information in its retrieval  
layer. Then, the retrieved information is 
assembled into context – basically added to  
the prompt. Only after that, the language 
model itself gets involved. And finally,  
the model generates an answer 
grounded in what it was given.
This is the important bit: the 
language model never goes out to  
search on its own. All the “looking things 
up” happens before the model is even called.
Now let’s zoom into the most important part 
of RAG – the Retrieval Layer. This is where  
most of the flexibility and the power comes from.
Most RAG systems start by searching 
internal sources. That could be PDFs,  
knowledge bases, company documents, or 
structured datasets. A RAG system uses  
vectors to look for the most relevant pieces 
of text based on meaning, not keywords.
However, the retrieval can go even further. 
Instead of stopping at internal data,  
some systems also pull in external sources, 
like public databases or even live web data.  
For example, something like the Oxylabs Web 
Scraper API can be plugged into the Retrieval  
Layer to fetch real-time information from the 
web, such as e-commerce data or SERP results.
Whichever path your flow takes, that data doesn’t 
magically update the model. It’s simply retrieved,  
cleaned, and then injected as context before 
generation. So the model still does what it’s  
best at – reasoning and language – but now 
it’s working with up-to-date information.
Just keep in mind: all this 
retrieval happens outside the model,  
and whatever comes back is 
passed into the main pipeline.
Once it’s done, the system uses both the 
user query and the system’s instructions to  
assemble context into a single upgraded 
input prompt. This is why RAG works,  
not because models are smarter, but 
because their inputs are better.
And the best part, this same 
process can be used for many  
applications. Customer support bots, 
product assistants, research tools,  
or internal search systems. The logic stays 
the same. The only thing changing is the data.
So why does all of this matter? RAG gives 
you up-to-date answers, fewer hallucinations,  
access to private or proprietary data, and 
more control over where information comes from.
And maybe most importantly, you don’t need to 
retrain the model. If your data changes, you just  
update the source or change what gets retrieved. 
That makes RAG much faster, incredibly cheaper,  
and easier to maintain than most alternatives, 
such as LLM fine-tuning or full model retraining.
So if you take one thing away from this video, 
let it be this: RAG isn’t a futuristic technique.  
It’s how the best AI systems already work 
today. Retrieval first, generation second.
Remember – when ChatGPT searches the 
web, when you upload a PDF to Claude,  
or when Perplexity shows citations – 
that’s Retrieval-Augmented Generation  
in action. Once you see that, a lot of 
modern AI suddenly makes much more sense.
Wanna learn how to build your own 
RAG chatbot with OpenAI and web  
scraping? We have a whole step-by-step 
guide linked in the description below.
Or if you have any questions on how 
data can improve your AI workflows,  
email us at support@oxylabs.io or write 
to us via the live chat on our homepage.
Also, don’t forget to subscribe to our 
channel for more videos like this one  
and consider joining our Discord community, 
where we discuss all things web scraping.
Thank you for watching, and 
see you in the next one.