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Why is chunking important when building a RAG pipeline?

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RRitesh

Last updated: December 3, 2025 at 18:27

To improve GPU utilization

To break documents into smaller, meaningful segments for better retrieval

To reduce costs of vector DBs

To avoid the need for embeddings

Explanation:

Explanation

Chunking is a critical preprocessing step in RAG (Retrieval-Augmented Generation) pipelines for several reasons:

Better retrieval accuracy: Breaking documents into smaller, meaningful segments allows for more precise retrieval of relevant information from the vector database.
Context preservation: Proper chunking maintains semantic coherence within each chunk, making embeddings more accurate.
Efficient processing: Smaller chunks are easier to embed and store in vector databases.
Improved relevance: When a user query comes in, the system can retrieve the most relevant chunks rather than entire documents.

A. To improve GPU utilization: While chunking might indirectly affect GPU usage, this is not its primary purpose.
C. To reduce costs of vector DBs: Chunking doesn't necessarily reduce costs; it might even increase storage needs due to more embeddings.
D. To avoid the need for embeddings: Chunking doesn't eliminate the need for embeddings; in fact, each chunk still needs to be embedded for retrieval.