Chunking is the process of splitting documents into smaller segments for embedding and retrieval in RAG systems, representing a critical design decision that significantly impacts retrieval quality and generation accuracy.
The core tension: smaller chunks are more precise, retrieving exactly the relevant sentence rather than surrounding irrelevant content, but may lack context needed for understanding. Larger chunks provide more context but may contain irrelevant information that dilutes relevance scores or confuses the generator.
, 500 tokens with 50-token overlap), simple and predictable but potentially cutting sentences or ideas mid-thought. Sentence-based chunking respects linguistic boundaries but produces variable-length chunks. Semantic chunking uses embeddings to identify topic shifts, keeping coherent ideas together.
Hierarchical chunking creates multiple granularities: paragraphs for broad retrieval, sentences for precise matching. Document structure-aware chunking respects headings, sections, and formatting, keeping tables and lists intact. Overlap between chunks guarantees that information split across boundaries remains retrievable.
The optimal strategy depends on document type, query patterns, and the embedding model's characteristics. Dense documents like technical manuals may need smaller chunks; narrative content may benefit from larger ones. Chunking decisions compound through the RAG pipeline: poor chunking creates retrieval failures that no amount of generation quality can compensate for.
Interactive Visualizer
Chunking Strategy Visualizer
Explore how chunk size affects retrieval precision and context in RAG systems
Document Chunks
Machine learning algorithms are computational methods that enable systems to learn patterns from data without explicit programming. These algorithms form the foundation of artificial intelligence applications. There are three main types: supervised learning uses labeled data to train models, unsupervised learning finds hidden patterns in unlabeled data, and reinforcement learning
learns through trial and error interactions. Popular algorithms include neural networks, decision trees, and support vector machines. Neural networks consist of interconnected nodes that process information similarly to biological neurons. Decision trees use branching logic to make predictions based on feature values. Support vector machines find optimal boundaries between different
classes of data. The choice of algorithm depends on the problem type, data characteristics, and performance requirements.