- Embeddings
- Vector Databases
- RAG
Now we know what RAG is.
But an important question remains:
How does a RAG system actually work from beginning to end?
To understand this, we need to understand the RAG Pipeline.
What Is a RAG Pipeline?
In simple words:
A RAG pipeline is the step-by-step process used to take information from documents, find the information relevant to a user's question, and give that information to an LLM to generate an answer.
A simple RAG pipeline looks like this:
Documents → Chunks → Embeddings → Vector Database → User Query → Retrieval → Context → LLM → Answer
Let's understand each step with one real-world example.
Real-World Example: College Student Handbook 🎓
Imagine a college has a large Student Handbook.
The handbook contains information about:
- Attendance
- Exams
- Fees
- Leave
- Hostel
- Library
- Academic rules
One rule says:
“Students must maintain at least 75% attendance to be eligible for the final examination.”
Now a student asks an AI assistant:
“How much attendance do I need to write my final exam?”
Let's see what happens behind the scenes.
Step 1: Collect the Documents 📄
First, the RAG system needs a source of information.
In our example, the source is:
College Student Handbook
It could be a:
- Word document
- Website
- Text file
- Knowledge base
The system first takes this information and prepares it for processing.
Step 2: Split the Document Into Chunks ✂️
A large document can contain hundreds or thousands of pages.
Instead of treating the entire document as one huge piece of text, we divide it into smaller sections called chunks.
For example:
Chunk 1 — Attendance
“Students must maintain at least 75% attendance to be eligible for the final examination.”
Chunk 2 — Leave
“Students can apply for medical leave according to the college leave policy.”
Chunk 3 — Examination Fees
“Semester examination fees must be paid before the specified deadline.”
Now the information is divided into smaller, manageable pieces.
Step 3: Create Embeddings 🔢
Each chunk is converted into an embedding.
An embedding is a numerical representation of the meaning of the text.
For example:
Attendance Rule
becomes something conceptually like:
[0.12, -0.45, 0.78, 0.31, ...]
We don't need to understand each number.
The important idea is:
Text → Embedding
Embeddings allow the system to compare pieces of information based on their meaning.
Step 4: Store Embeddings in a Vector Database 🗄️
The generated embeddings are stored in a Vector Database.
The database can store:
- Embeddings
- Original text or a reference to it
- Document information
- Metadata
For example:
Attendance Rule → Embedding
Leave Rule → Embedding
Exam Fee Rule → Embedding
Now the information is ready to be searched.
Step 5: User Asks a Question ❓
Now the student asks:
“How much attendance do I need to write my final exam?”
This is called the user query.
The RAG system now needs to find which information from the knowledge base is relevant to this question.
Step 6: Convert the Query Into an Embedding 🔢
The user's question can also be converted into an embedding.
So:
“How much attendance do I need to write my final exam?”
Query Embedding
This allows the system to compare the question with the embeddings stored in the Vector Database.
Step 7: Search the Vector Database 🔍
Now the system searches the Vector Database.
It compares the query embedding with the stored embeddings.
The system looks for information that is semantically similar or relevant to the question.
It finds:
“Students must maintain at least 75% attendance to be eligible for the final examination.”
This is the retrieval step.
The system has successfully found the relevant information.
Step 8: Retrieve the Relevant Context 📚
The retrieved information is now collected as context.
For our example:
User Question
“How much attendance do I need to write my final exam?”
Retrieved Context
“Students must maintain at least 75% attendance to be eligible for the final examination.”
This context contains the information the LLM needs to answer the question.
Step 9: Send the Question + Context to the LLM 🧠
Now the RAG system gives the LLM:
Retrieved Context
The LLM can now understand the question and use the retrieved information to generate the answer.
Conceptually:
Question + Relevant Information → LLM
Step 10: Generate the Final Answer 💬
The LLM generates a natural-language response.
For example:
“You need at least 75% attendance to be eligible for the final examination.”
This is the final answer shown to the student.
The Complete RAG Pipeline
Now let's put everything together
This is the basic RAG pipeline.
Let's Understand the Pipeline in One Example
The student asks:
“How much attendance do I need to write my final exam?”
The system does:
1. Search
Find information related to attendance.
2. Retrieve
Find:
“Students must maintain at least 75% attendance...”
3. Provide Context
Give the retrieved information to the LLM.
4. Generate
The LLM generates:
“You need at least 75% attendance to be eligible for the final examination.”
That's the complete process.
Why Do We Need Chunking?
You may wonder:
“Why not store the entire document as one embedding?”
Imagine a 500-page college handbook.
It contains many different topics.
If the entire document is treated as one large piece, it becomes difficult to retrieve only the exact information needed.
By dividing it into smaller chunks:
Attendance → one chunk
Leave → another chunk
Hostel → another chunk
Exam fees → another chunk
the system can retrieve more relevant information.
Why Do We Need Embeddings?
Suppose the student asks:
“How many classes should I attend before my final exam?”
But the handbook says:
“Students must maintain at least 75% attendance to be eligible for the final examination.”
The exact words are different.
However, the meaning is related.
Embeddings help represent the meaning of the text so the system can perform semantic search.
Why Do We Need a Vector Database?
Once we create embeddings for thousands of chunks, we need a system that can efficiently store and search them.
That's where the Vector Database comes in.
It helps the RAG system find the most relevant information for the user's question.
So:
Embeddings represent the information.
Vector Database stores and searches those representations.
Why Do We Need an LLM?
The Vector Database is good at finding information.
But it doesn't normally act like a conversational assistant.
The LLM takes the retrieved information and turns it into a natural-language answer.
So we can think of it like this:
Vector Database → Find the information
LLM → Explain the information
Together, they can create a useful AI assistant.
RAG Pipeline vs Normal Search
Traditional search might return:
“Attendance Policy — Page 47”
The user still needs to open the document and read it.
A RAG system can retrieve the relevant section and use an LLM to generate an answer such as:
“You need at least 75% attendance to be eligible for the final examination.”
So RAG combines:
Information Retrieval + LLM Generation
Another Real-World Example: Company Documents 🏢
Imagine a company has thousands of internal documents.
An employee asks:
“How many annual leave days do employees get?”
The RAG pipeline can work like this:
Company Documents↓Chunks↓Embeddings↓Vector Database↓Employee Question↓Retrieve Relevant Leave Policy↓LLM↓Answer
For example:
“According to the company policy, employees are eligible for 20 days of annual leave per year.”
The same pipeline can be used for many types of knowledge-based AI applications.
What Happens If the Wrong Information Is Retrieved?
This is an important point.
RAG depends heavily on the quality of retrieval.
Imagine the student asks about:
Attendance
but the system retrieves:
Hostel Rules
The LLM may not have the correct information needed to answer the question.
This is why good:
- Chunking
- Embeddings
- Retrieval
- Search configuration
- Data quality
are important for building a good RAG system.
RAG Pipeline in Simple Words
You don't need to remember all the technical terms immediately.
Just remember this story:
We have documents.↓Break them into smaller pieces.↓Convert those pieces into embeddings.↓Store them in a Vector Database.↓User asks a question.↓Convert the question into an embedding.↓Search for relevant information.↓Give the retrieved information to the LLM.↓LLM generates the final answer.
That's a RAG pipeline.
The Connection Between All Our Previous Topics
Now our previous concepts connect together.
Embeddings
Convert text into numerical representations.
Vector Database
Stores and searches those representations.
Retrieval
Finds relevant information.
RAG
Combines retrieved information with an LLM.
LLM
Generates the final response.
So the complete idea is:
Documents → Chunks → Embeddings → Vector Database → Retrieval → Context → LLM → Answer
One-Line Definition
A RAG pipeline is a step-by-step process where a system retrieves relevant information from external data and provides it to an LLM to generate a useful answer.
Conclusion
A RAG pipeline connects several important AI concepts together.
It starts with external information such as documents.
That information is:
Chunked → Embedded → Stored → Retrieved → Given to the LLM → Used to Generate an Answer
Using our college example:
Student Question → Find Attendance Rule → Retrieve 75% Rule → Give Context to LLM → Generate Answer
This is how a simple RAG-based AI assistant can answer questions using information from a specific knowledge source.
Now we have a complete high-level understanding of the RAG architecture.
In the next post, we'll take one important part of this pipeline and understand it more deeply:
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