Understanding RAGs – From Chunking to Vectorization (With Real-Life Examples)

Ever heard of RAGs (Retrieval-Augmented Generation) and felt like it was rocket science? Don’t worry — in this blog, we’ll break it down using real-world analogies so even a non-techie can understand.

We'll explain:

• What is Indexing?
• Why Vectorization Matters
• Why RAGs Exist
• What is Chunking?
• Why Do We Overlap Chunks?

Whether you're a student, content creator, or developer — this one's for you.

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What is Indexing?

Analogy:

Imagine running a mobile accessories shop in Bengaluru.

Every product — chargers, cables, cases — is labeled and placed neatly on shelves.

That’s how you find items quickly.

In AI:

• Indexing helps the system quickly find the most relevant information.
• It's like creating a catalog for fast lookup.

Without indexing, AI would have to “scan the entire shop” every time you ask something.

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Why Do RAGs Exist?

Generative AI like GPT is powerful but doesn’t know your private data like:

• PDFs
• Website content
• Helpdesk FAQs
• Internal documents

That's where RAG comes in.

**RAG = Retrieval + Generation**

1. **Retrieves** the most relevant chunks from your data
2. **Generates** an answer using GPT with that info as context

RAG = AI with your company’s brain + GPT’s intelligence

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Why Do We Perform Vectorization?

Analogy:

A customer walks in and says:

“Give me the best 5G phone under ₹15,000 with good battery.”

Your assistant understands:

• Budget
• 5G requirement
• Battery preference

AI needs this semantic understanding too.

Vectorization converts text → numerical meaning (vectors)

This enables semantic search, not just keyword search.

Example:

from sentence_transformers import SentenceTransformer
model = SentenceTransformer('all-MiniLM-L6-v2')
vectors = model.encode(["good battery", "long-lasting power"])
print(vectors)

“good battery” ≈ “long-lasting power” in vector space — because they *mean* the same.

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Why Do We Perform Chunking?

Problem:

LLMs like GPT have token limits — they cannot read 200 pages at once.

Solution:

Break documents into smaller pieces called chunks.

Analogy:

When reading a new mobile catalog, you don’t read the entire book at once.

You split it into:

• Samsung Phones
• iPhones
• Chargers
• Earphones

That’s chunking!

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Why Do We Overlap Chunks?

Sometimes important info sits at the boundary:

• End of one paragraph
• Start of the next

Strict splitting may cause AI to lose context.

Example:

Chunk 1: Info A, B, C

Chunk 2: Info C, D, E

The shared C preserves meaning.

def chunk_with_overlap(text, chunk_size=200, overlap=50):
    chunks = []
    start = 0
    while start < len(text):
        end = start + chunk_size
        chunks.append(text[start:end])
        start += chunk_size - overlap
    return chunks

Overlap helps the AI understand continuity.

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Summary Table

| Concept | Analogy in Mobile Shop | Purpose in RAGs |

|---------------|----------------------------------|-------------------------------------|

| Indexing | Labeling shelves | Fast retrieval of chunks |

| Vectorization | Understanding customer intent | Semantic search |

| RAGs | Staff + catalog + answers | Combine private data + GPT |

| Chunking | Splitting catalog into sections | Fit data into LLM limits |

| Overlap | Repeating edge info | Preserve meaning across chunks |

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✅ Final Thought

RAGs are not complicated.

They're simply a smart way of making AI feel more like a real expert — one who:

• Understands your data
• Remembers context
• Provides accurate answers

Start simple.

Play with chunking + vectorization.

Soon you’ll be building powerful knowledge-enhanced AI apps!