Chunking strategy

In AI, chunking strategy refers to the process of dividing text into smaller, meaningful segments or “chunks.” This can be done using two main approaches: structural chunking, which breaks down text based on predefined structures such as paragraphs or sentences, and semantic chunking, which groups text by meaning and context. While semantic chunking allows for deeper understanding, structural chunking is more straightforward and commonly used due to its simplicity and efficiency in many AI applications. Both strategies help improve the processing, analysis, and comprehension of large text bodies in AI systems.