RAG - Chunking

Short summary

Chunking breaks data into meaningful pieces before embedding in RAG systems, improving retrieval accuracy by preventing unrelated content. Four strategies exist: fixed (simplest), overlapping (semantic context), semantic (meaning-based), and embedded (model-based). Best method depends on dataset type and requires trade-off analysis.

• •Chunking prevents irrelevant data from being retrieved in RAG systems by splitting data into meaningful pieces
• •Four methods exist with different trade-offs: fixed, overlapping, semantic, and embedded chunking
• •Choosing the right strategy depends on dataset type and requires trial-and-error to optimize retrieval performance

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