15 RAG Chunking Techniques Every AI Engineer Should Know

Author(s): Tarun Singh

Originally published on Towards AI.

Level Up Your RAG Apps: 15 Easy Chunking Strategies (with Examples!)

Retrieval-Augmented Generation (RAG) depends heavily on how we chunk our data.
If you want the LLM to retrieve context that actually makes sense, you must chunk your data thoughtfully.

This article outlines 15 techniques for chunking data effectively in Retrieval-Augmented Generation (RAG) systems. Each strategy is designed to help AI engineers present information in a way that enhances the understanding and retrieval context by splitting data thoughtfully based on structure, content type, and context relevance. By employing these strategies, users can optimize how large language models access and process information.

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Published via Towards AI