# What is Chunking? Definition & Technical Mechanics

> **Source:** [SPIDITS AI AI Glossary](https://spidits.com/ai-glossary)  
> **Category:** Information Retrieval  
> **Canonical Citation:** [https://spidits.com/ai-glossary/chunking](https://spidits.com/ai-glossary/chunking)

## Definition
Chunking is the process of breaking down a large, continuous document into smaller, manageable, and semantically cohesive text fragments (chunks) before indexing them in a vector database.

## Architectural Mechanics & Deep Dive
Chunking is the process of breaking a large document into smaller, coherent, and semantically meaningful segments prior to generating vector embeddings. In Retrieval-Augmented Generation (RAG), effective chunking is critical. If chunks are too small, they lose vital context; if they are too large, they dilute the specific information needed and may exceed the LLM's prompt token limit, necessitating strategies like sliding window overlaps.

## Industry Applications & Real-World Use Cases
RAG database preparation, document parsing, and token limit compliance.

## Frequently Asked Questions

### What is chunk overlap in RAG?
The practice of keeping a few overlapping sentences between adjacent chunks to ensure semantic context is not cut off at boundary lines.

### What are the main chunking strategies?
Fixed-size chunking (splitting by token count), markdown-aware chunking (splitting by headers), and semantic chunking (splitting based on embedding changes).

## Related Terminology
- [rag](https://spidits.com/ai-glossary/rag)
- [vector-database](https://spidits.com/ai-glossary/vector-database)
- [embedding](https://spidits.com/ai-glossary/embedding)

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*From the SPIDITS AI Knowledge Base (336+ terms indexed). Explore full technical definitions: [https://spidits.com/ai-glossary](https://spidits.com/ai-glossary)*