AI models and agents

Chunking

Also known as: splitting into passages

Dividing material into sections for retrieval or model processing.

A practical example

Documentation is split along coherent sections and topics.

What should I know?

Chunks that are too small may lose conditions and context.

AI models and agents in practice

A model processes information; the application defines tools and permissions. Reliable answers require good sources and verification. Actions involving accounts, content, and orders must stay within the granted scope.

Related Semly features

See where this concept applies when working with Semly.

Semly MCPConnect Semly data and tools to your AI assistant. Analyze reports and work with Leon in a conversation.Websites for AI agentsShare structured company knowledge and inquiry forms through an agent channel.

Related terms

RAGCombining retrieval of relevant materials with generating an answer from them.Context windowThe amount of information a model can consider at once when answering, within system limits.RetrievalSelecting documents or passages needed to handle a query.

Documentation and further reading

Definition prepared by Semly. Standards and behavior are described by their creators:

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