What is Chunking strategy?
Chunking strategy is a retrieval or retrieval-augmented generation concept. It is part of the vocabulary teams use when they compare models, design prompts or review an AI system.
Definition
Chunking strategy is a retrieval or retrieval-augmented generation concept. It is part of the vocabulary teams use when they compare models, design prompts or review an AI system. The term is used in retrieval and rag discussions, where precision matters: two teams can use the same word to mean slightly different things. In this glossary, chunking strategy is treated as a practical concept rather than a formal specification.
Why it matters
Retrieval terms describe how external documents are indexed, searched and inserted into a prompt. They matter because most production systems rely on fresh or private data that is not in the model's weights.
Example
Suppose you are comparing Llama 3 Baseten 8B with another model for a production task. Chunking strategy is one of the factors behind the numbers you see: Llama 3 Baseten 8B lists a 4,096-token context window, costs $1.02 per million input tokens and scores 67.1 on IFEval. Understanding the term helps you decide which of those differences actually matter for your workload.
How to evaluate it
- Check whether chunking strategy is defined the same way in the documentation you are reading.
- Ask which measurement or trade-off the term is describing before comparing numbers.
- Separate vendor claims from independently observed behaviour on your own data.
- Write down the definition your team uses so reviews and evaluations stay consistent.
Related terms
Related guides
FAQ
What does Chunking strategy mean?
Chunking strategy is a retrieval or retrieval-augmented generation concept. It is part of the vocabulary teams use when they compare models, design prompts or review an AI system.
Why does Chunking strategy matter?
Retrieval terms describe how external documents are indexed, searched and inserted into a prompt. They matter because most production systems rely on fresh or private data that is not in the model's weights.
How is Chunking strategy used in practice?
Suppose you are comparing Llama 3 Baseten 8B with another model for a production task. Chunking strategy is one of the factors behind the numbers you see: Llama 3 Baseten 8B lists a 4,096-token context window, costs $1.02 per million input tokens and scores 67.1 on IFEval. Understanding the term helps you decide which of those differences actually matter for your workload.