The Effect of Text Chunk Size on Retrieval-Augmented Generation Performance
Retrieval-Augmented Generation (RAG) systems have emerged as a powerful process for allowing large language models (LLMs) to retrieve relevant information to use as source material during text generation. A critical yet under-explored component of these systems is the granularity at which source documents are segmented into retrievable chunks. The size of these chunks has the potential to significantly influence generation quality, contextual correctness, retrieval precision, and computational efficiency. Despite its importance, chunk size is often selected without proper evaluation of its impact on generation quality. Smaller chunks, such as individual sentences, may allow for precise re...
| Authors | German Garrido-Lestache Belinchon, Hugo Garrido-Lestache Belinchon |
|---|---|
| Primary category | cs.IR |
| Published | 8 June 2026 |
| Last updated at source | 8 June 2026 |
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