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Vector Databases: Pinecone, Weaviate & pgvector · Lesson

Chunking Strategies for RAG

Learn how to split documents into effective chunks for embedding so your RAG system retrieves precise, relevant context.

Chunking Strategies for RAG is a free Vector Databases: Pinecone, Weaviate & pgvector lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Vector Databases: Pinecone, Weaviate & pgvector learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.

Why Chunking Matters

Before embedding, documents are split into chunks. Chunk quality directly determines retrieval quality — too large dilutes relevance, too small loses context.

Fixed-Size Chunking

The simplest method splits text every N characters or tokens. Fast but can cut sentences mid-thought.

def fixed_chunks(text, size=500):
    return [text[i:i+size] for i in range(0, len(text), size)]

Overlapping Windows

Add overlap between chunks so context spanning a boundary is not lost. A 10-20% overlap is common.

def overlap_chunks(text, size=500, overlap=100):
    step = size - overlap
    return [text[i:i+size] for i in range(0, len(text), step)]

Sentence-Aware Splitting

Split on sentence boundaries so each chunk stays grammatically whole. This usually improves embedding quality over raw character splits.

import re
sentences = re.split(r'(?<=[.!?])\s+', document_text)

Recursive Chunking

Recursive splitting tries large separators first (paragraphs), then smaller ones (sentences, words) until chunks fit the size limit. It respects natural structure.

Semantic Chunking

Semantic chunking groups sentences by meaning similarity, starting a new chunk when topic shifts. More expensive but very precise.

Chunk Size vs Model

Match chunk size to your embedding model's context window and your LLM's prompt budget.

  • Small chunks: precise but fragmented
  • Large chunks: rich but noisy

Keeping Metadata

Store source, page, and position metadata with each chunk so you can cite sources and reconstruct context later.

chunk = {
  'text': part,
  'source': 'guide.pdf',
  'page': 4,
  'position': idx
}

Parent-Child Chunks

Embed small child chunks for precise matching, but return their larger parent chunk to the LLM for full context. Best of both worlds.

Evaluating Chunking

Test different chunking configs with the same queries and measure retrieval hit rate. There is no universal best — measure for your data.

Practical Defaults

A solid starting point:

  • Recursive splitting
  • ~500 tokens per chunk
  • ~50-100 token overlap
  • Attach source metadata

Quick Check

Test your chunking knowledge.

Recap

You learned fixed, overlapping, sentence-aware, recursive, and semantic chunking, plus parent-child retrieval and metadata. Good chunking is the foundation of accurate RAG retrieval.

Frequently asked questions

Is the “Chunking Strategies for RAG” lesson free?

Yes — the full text of “Chunking Strategies for RAG” is free to read here on the web, and the Vector Databases: Pinecone, Weaviate & pgvector course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Vector Databases: Pinecone, Weaviate & pgvector course, upgrade to CoddyKit PRO.

What will I learn in “Chunking Strategies for RAG”?

Learn how to split documents into effective chunks for embedding so your RAG system retrieves precise, relevant context. You practise Vector Databases: Pinecone, Weaviate & pgvector with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

Do I need any experience to start Vector Databases: Pinecone, Weaviate & pgvector?

No prior experience is required. Vector Databases: Pinecone, Weaviate & pgvector on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “Chunking Strategies for RAG” lesson take?

Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.

Can I write and run code in this Vector Databases: Pinecone, Weaviate & pgvector lesson?

Yes. Every Vector Databases: Pinecone, Weaviate & pgvector lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.

All lessons in this course

  1. RAG System Architecture Overview
  2. Integrating with LLM Frameworks
  3. Contextual Information Retrieval
  4. Chunking Strategies for RAG
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