What is the Recommended Chunk Size?

In the context of building a Retrieval-Augmented Generation (RAG) pipeline or any AI application utilizing a vector store, determining the optimal chunk size is crucial for balancing retrieval precision and context quality. Chunking refers to dividing large text into smaller, meaningful segments, with chunk size typically measured in tokens. Smaller chunks enhance retrieval precision by focusing on specific ideas, while larger chunks increase recall but may introduce irrelevant information into the LLM's context window. The choice of chunk size is influenced by factors such as the embedding model's token limit, the LLM's context window, document type, query style, and retrieval strategy. Recommended starting points vary by use case, with general-purpose RAG systems benefiting from chunks of 512–1,024 tokens with overlap, whereas short-form content and technical documents require different sizes to maintain semantic coherence. Testing and tuning chunk sizes as hyperparameters can optimize retrieval quality, taking into account the specific characteristics of the documents, queries, and the retrieval architecture used. SurrealDB's vector search capabilities, combined with graph and relational features, provide a comprehensive solution for building robust RAG pipelines.