Chunking Strategies

Chunking strategies are methods for dividing text into smaller, manageable segments (chunks) to improve information retrieval and processing in applications like retrieval-augmented-generation-rag.

Key Concepts

• Fixed-size chunking: Divides text into chunks of equal length.
• Semantic chunking: Considers meaning and context to create chunks.
• Overlapping chunks: Chunks that share some content to preserve context.
• Sliding window: A technique where chunks are created by moving a fixed-size window across the text.

Applications

• Essential for optimizing retrieval-augmented-generation-rag applications.
• Improves performance in information retrieval systems.

Related Work

• Adam Lucek - optimal RAG chunking with ChromaDB
  • Video: Optimal RAG Chunking with ChromaDB
  • Explores various text chunking strategies for RAG.
  • Presents insights from a ChromaDB technical report titled “Evaluating Chunking Strategies for Retrieval.”
  • Details different methods, implementations, and performance findings.

Backlinks

• 2026 04 14 Adam Lucek optimal RAG chunking with ChromaDB

Source Notes

• 2026-04-14: # Discover AI channel - Graph RAG evolved --- --- https://www.youtube.com/watch?v=oetP9uksUwM This video provides a comprehensive overview of the evolution of Retrieval-Augmented Generation (RAG) systems, from foundational RAG to GraphRAG, Light (Discover AI channel - Graph RAG evolved)