Chunking Strategies for RAG
Learn how to split documents into effective chunks for embedding so your RAG system retrieves precise, relevant context.
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)]All lessons in this course
- RAG System Architecture Overview
- Integrating with LLM Frameworks
- Contextual Information Retrieval
- Chunking Strategies for RAG