RAG, Part 1 — Chunking Strategies

Author(s): Deepak Chahal

Originally published on Towards AI.

Why the way you chunk data shapes what your RAG system retrieves

Most of you might have heard the term RAG (Retrieval Augmented Generation). As the name suggests, it retrieves external information, augments the model’s knowledge, and then generates a response.

This article explores the concept of Retrieval Augmented Generation (RAG) and emphasizes the importance of data chunking. It outlines several strategies for chunking data effectively, detailing methods such as character splitting, recursive character splitting, and document-specific chunking. The article further highlights semantic chunking, which adapts chunk size based on context, and agentic splitting, offering a more intuitive approach to chunking by mimicking human editing techniques. Lastly, it discusses the role of embeddings and vector databases in optimizing RAG systems for better retrieval accuracy and contextual understanding.

Read the full blog for free on Medium.

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Published via Towards AI