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DevelopmentIntermediate2020s

Chunking

Chunking

Chunking means splitting a large document into smaller pieces so an AI can search and use it more effectively.

Explained simply

Chunking means splitting a large document into smaller pieces so an AI can search and use it more effectively.

1Large document
→
2Split into pieces
→
3Search relevant chunks
→
4Use in answer

At a glance

Category
Development
Difficulty
Intermediate
Introduced
2020s

Real example

A 200-page manual is split into short sections before a support assistant searches it.

Why it matters

It gives you a clear way to understand where this idea fits in AI and how it affects the tools you use.

Timeline

Origins

The ideas behind Chunking begin developing.

2020s

Chunking becomes a recognised term or technique.

Wider use

Research and practical applications increase.

Modern AI

Chunking becomes connected to newer AI systems and products.

Today

Chunking remains relevant in the development area of AI.

Dates for broad concepts may describe the period when the idea emerged or became widely used, rather than one exact invention date.

Learn next

Continue with these connected terms:

EmbeddingAn embedding turns meaning into a list of numbers, allowing an AI system to compare how similar things are.→Vector DatabaseA vector database stores embeddings so a system can quickly find items with similar meaning.→RAGRAG lets an AI search trusted information before it writes an answer. RAG stands for Retrieval-Augmented Generation.→

Simple infographic

A quick visual way to understand Chunking.

1Large document
→
2Split into pieces
→
3Search relevant chunks
→
4Use in answer
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