Chunking (the process of breaking documents into smaller pieces for embedding and retrieval) is one of the most important and often overlooked design decisions in a RAG system. The goal is to strike a balance: chunks should be large enough to preserve meaning and context, but small enough to allow precise retrieval. Poor chunking can lead to irrelevant or incomplete results, even if the underlying model is strong.

In practice, chunking strategies should align with the structure of your data. For example, paragraph- or section-based chunking often works better than fixed-length splits, especially for narrative or policy documents. It’s also common to include overlap between chunks to preserve context across boundaries and improve retrieval continuity. The right approach depends on your use case, so it’s important to test different strategies and evaluate their impact on retrieval quality early in development.

Additional Resources:

• Chunking strategies for LLM applications (pinecone.io)
• Text splitter integrations (langchain)
• Unleashing the power of LangChain Text Splitters: Techniques and Best Practices (Arsturn)

This response has been generated by an LLM based on notes from PJMF technical consultations. All responses go through human review by our PJMF Products & Services team and are anonymized to protect our consultation participants.