Design a chunking strategy for RAG

Q: Design a chunking strategy for RAG

This question evaluates understanding of chunking strategies for Retrieval-Augmented Generation systems, testing competencies in information retrieval, embedding and indexing trade-offs, document-structure-aware segmentation, and semantic chunking within the ML System Design and NLP domains.

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Question

You are building a Retrieval-Augmented Generation (RAG) system that uses an LLM plus a vector database. Before creating embeddings and indexing documents, you must split long documents into chunks.

Describe how you would design the chunking strategy. In your answer, discuss:

How you would choose chunk size and overlap and the trade-offs involved (recall vs. context size, latency, etc.).
How you would use document structure (e.g., headings, paragraphs, sections) vs. naive fixed-length splits.
When you might use more advanced methods like semantic chunking or dynamic chunk sizes.
How you would evaluate and iterate on your chunking strategy in a real system.

Design a chunking strategy for RAG

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Design a chunking strategy for RAG

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