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How to Optimize Chunk Size for RAG in Production?
Artificial Intelligence   Data Science   Latest   Machine Learning

How to Optimize Chunk Size for RAG in Production?

Last Updated on May 14, 2024 by Editorial Team

Author(s): Mandar Karhade, MD. PhD.

Originally published on Towards AI.

The chunk size can make or break the retrieval. Here is how to determine the best chunk size for your use case.

Today, we will examine chunk-size optimization during the development of an RAG application. We will assume that it is a business-specific use case. We will also observe how and where generic approaches to chunk-size finding can fail or excel.

Let me ramble it a little bit! This part is important so that the decisions are apparent in the later part of the article.

Assume that you are working at your company. The company has a bunch of historical documents that are organized somewhere in SharePoint. Your company has finally decided to invest in Generative AI. Now, you are tasked with creating an application that can find relevant information in the form of answers and provide it to your 200 employees. Let’s chunk your task into smaller issues.

A store of documents (let's say you have 10000 documents)A way to retrieve informationA way to generate answerUI/UX to deliver answers back to your team/users

We will focus on only the store of documents and a way to retrieve information. Two critical issues in the production system are fault tolerance and Scalability/Latency —

There are two probabilities that we should be worried about. P1 is the probability of making a mistake, and P2 is the probability of harm… Read the full blog for free on Medium.

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Published via Towards AI

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