🌟 Day 4 – Diving Into the First Pillar: Chunking

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A few weeks ago, I wrote a post saying I had zero idea what “chunking” even was. Now, a few weeks later I definitely understand more, but not enough.That’s why today is fully dedicated to Pillar 1: Chunking.

Back then I got great examples:

🍞 “Slice a loaf of bread into pieces.”

🍕 “Cut a pizza into slices.”Perfect analogies — and still true.

But now I understand why chunking is so important:

🔹 What Chunking Really Is

Chunking is the most critical preprocessing step in any RAG system. IT means breaking large documents into smaller, meaningful segments (“chunks”), which are then embedded, indexed, and retrieved later.

Chunks are the atomic information units your RAG system uses.

If the chunks are bad, retrieval is bad — and the LLM can’t fix it.

🔹 The Core Dilemma

Chunking is always a balance between:

1️⃣ Precision – smaller chunks give cleaner embeddings

2️⃣ Context – bigger chunks give more meaning to the LLM

Too big → diluted meaningToo small → missing context→ And THAT is the hardest challenge in chunking.

🔹 Best Practices for Chunking

Here are the key strategies I’m learning:

📌 Recursive Character

ChunkingRespects natural text boundaries (paragraphs, sentences).Often the recommended default.

📌 Overlap (10–20%)