In the context of building LLM-related applications, chunking is the process of breaking down large pieces of text into smaller segments. It’s an essential technique that helps optimize the relevance of the content we get back from a vector database once we use the LLM to embed content. In this blog post, we’ll explore if and how it helps improve efficiency and accuracy in LLM-related applications.
As we know, any content that we index in Pinecone needs to be embedded first. The main reason for chunking is to ensure we’re embedding a piece of content with as little noise as possible that is still semantically relevant.
This is a companion discussion topic for the original entry at https://www.pinecone.io/learn/chunking-strategies/