Chunking Strategies in RAG systems

KEEP IN TOUCH | THE GEN AI SERIES

Rahul S

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Chunking is about splitting a text into smaller, more homogeneous units, called chunks, which can be easily processed by a language model (LLM).

Chunking plays a key role in optimizing semantic responses, as it allows us to reduce the complexity of the text and increase the relevance of the content.

Chunking also makes it easier to embed vector content via LLM, as it allows text to be represented in a multidimensional space, where each chunk has a specific position and direction.

This makes it possible to compare and associate chunks with queries, and thus generate targeted and relevant responses.

However, the choice of chunking strategy is not trivial. It depends on several factors, such as

  • the type and amount of data,
  • the nature and complexity of the queries, and
  • the characteristics and performance of the model.

In addition, the choice of chunking type has a significant impact on the final application, in terms of quality, efficiency, and scalability.

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