Tag: Chunking
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Optimizing AI Retrieval: The Science Behind Effective Chunking
In the realm of AI applications, document chunking is a pivotal pre-processing step that divides extensive texts into manageable units for efficient retrieval and processing by large language models (LLMs). Despite its widespread use, the impact of different chunking strategies on retrieval performance has not been thoroughly examined. Chroma Research’s technical report, “Evaluating Chunking Strategies…
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Chunking Strategies for LLM Applications: A Comprehensive Guide
In the rapidly evolving landscape of Large Language Models (LLMs), one technique stands out as a cornerstone for building efficient applications: chunking. This fundamental process involves breaking down larger texts into smaller, manageable segments, a strategy that is crucial in enhancing both the accuracy and efficiency of content retrieval from a vector database when leveraging…