Chunking Strategies - GeeksforGeeks Chunking is the process of segmenting text into smaller, manageable portions based on length, structure or semantic meaning It allows vector search to focus on precise information rather than entire documents
Chunking Strategies for AI and RAG Applications - DataCamp Semantic chunking is a meaning-aware technique that uses embeddings or semantic similarity to split text where topic shifts occur Instead of arbitrary boundaries, chunks are defined by meaning
Choosing the Right Chunking Strategy: A Comprehensive Guide to RAG . . . The agenticmemory library is a powerful Java-based RAG framework that provides nine really cool chunking strategies out of the box Unlike frameworks that force you into a single approach, agenticmemory recognizes that different documents require different strategies
Semantic vs Fixed Chunking: The Definitive Comparison Semantic chunking, sometimes called intelligent chunking, focuses on preserving the document's meaning and structure Instead of using a fixed chunk size, it strategically divides the document at meaningful breakpoints—like paragraphs, sentences, or thematically linked sections
Best Chunking Strategies for RAG (and LLMs) in 2026 Semantic chunking splits text based on meaning, not structure Instead of looking for paragraph breaks or sentence boundaries, it analyzes how related consecutive sentences are and creates chunks where topics shift
5 ways to improve your RAG — Part 1: Chunking strategies Semantic splitting is a more advanced chunking technique that aims to divide text into segments based on semantic meaning rather than fixed-size boundaries This approach leverages natural language processing techniques to identify where natural breaks occur within the text, such as between sentences or paragraphs