Document Chunking
Also called: Text Chunking, Document Segmentation
The process of splitting documents into smaller, semantically meaningful segments to improve indexing, retrieval, and context generation for AI systems.
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Related terms
The practice of sharing content between adjacent document chunks to preserve context and improve retrieval accuracy across chunk boundaries.
Embedding GenerationThe process of converting text, images, or other data into dense numerical vector representations that capture semantic meaning for retrieval, similarity search, and downstream AI tasks.
Indexing PipelineA sequence of processes that ingests, cleans, transforms, chunks, enriches, embeds, and indexes data into a searchable store, preparing documents and other content for efficient retrieval by AI applications.
RetrieverA system component responsible for fetching relevant documents or passages from a knowledge base given a query.
Retrieval-Augmented GenerationRAGAn architectural pattern that enhances generative model responses by retrieving relevant information from external knowledge bases.