Now that we understand what RAG is and why it is so popular, let’s understand how RAG actually works in real systems.

So now we will talk about the complete RAG pipeline.

RAG pipeline has two main components:

• First is called the ingestion pipeline


• Second is called the retrieval pipeline

These are very important concepts, but they are actually very simple once you understand them.

1. Ingestion Pipeline

Let’s first understand the ingestion pipeline.

Ingestion pipeline basically means how we prepare the data before giving it to the model.

You can think of it like preparing an open book before the exam.

We first prepare all the information in a structured format so that the model can later access it easily.

Step 1: Collect Data

First we collect all the data.

This data can be anything like:

• PDF files


• simple documents


• Excel files


• websites


• or even an entire company’s internal database

So basically, we take all possible sources of information.

Step 2: Extract Useful Data

Once we collect the data, the next step is extraction.

We extract useful text from all these sources.

For example:

• from PDFs we extract text


• from websites we extract content


• from databases we extract structured information

Now we have clean usable text.

Step 3: Split Data into Chunks

Now we split this data into small parts.

We do not process the whole document at once.

We divide it into small pieces called chunks.

For example, a document can be split into:

• chunk 1


• chunk 2


• chunk 3


• chunk 4

Each chunk contains a small meaningful portion of information.

Step 4: Convert Chunks into Embeddings

Now comes a very important step.

We convert each chunk into embeddings.

Now what are embeddings?

Basically, models cannot directly understand text. They only understand numbers.

So we convert text into numbers using embedding models.

Each chunk is converted into a vector of numbers, which represents its meaning.

Example:

"Refunds are allowed within 30 days"

→ [0.21, -0.67, 0.92, ...]

So now every chunk has:

• its original text


• and its embedding (vector representation)

Step 5: Store in Vector Database

Now we store all these embeddings in a vector database.

A vector database is different from a normal database.

Normal databases like MongoDB or MySQL use keyword-based search.

For example, if I search:

“heart attack symptoms”

I will only get documents that contain these exact words.

But vector databases work differently.

They use semantic search, which means meaning-based search.

So even if the exact words are not present, but the meaning is similar, it will still return results.

For example:

• “heart attack symptoms”


• “cardiac arrest signs”

Even if words are different, meaning is similar, so results will still be retrieved.

So this entire process of:

• collecting data


• extracting text


• chunking


• creating embeddings


• storing in vector database

is called the data ingestion pipeline.

Why It is Called Retrieval Augmented Generation

Now let’s understand the naming clearly.

It is called Retrieval Augmented Generation because:

• Retrieval → we first retrieve relevant documents from vector database


• Augmented → we add that retrieved context into the prompt


• Generation → the LLM generates the final response

So all three steps together form RAG.

Complete RAG Pipeline (Simple View)

Final Understanding

So now you should clearly understand:

• Ingestion pipeline is where we prepare data


• Retrieval pipeline is where we use that data at runtime


• Vector databases help us search based on meaning


• LLM generates answers using retrieved context

One Line Summary

RAG works by preparing data into embeddings during ingestion, storing it in a vector database, and retrieving relevant context at query time to help the LLM generate accurate answers.