WHAT IS DATA CHUNKING ?
Data chunking involves breaking down large volumes of data into smaller , more manageable chunks or segments . This process facilitates easier storage , retrieval and processing of information , enabling AI systems to handle vast data sets with enhanced efficiency . By partitioning data into manageable chunks , AI algorithms can operate more swiftly , reducing processing times and resource utilisation .
RAG combines pre-trained language models with a retrieval system that enables you to talk to your company ’ s own data . “ RAG is incredibly useful because it reduces hallucinations , helps LLMs to be more specific by using enterprise data and can be refreshed as quickly as new information becomes available . It ’ s also a more resource-efficient approach as the embedded retrieval inference costs are substantially lower than batch training custom models .”
To get RAG right , Couldwell suggests organisations should look at ‘ Day 1 ’ and ‘ Day 2 ’ problems . ‘ Day 1 ’ issues are those that exist around getting started , like preparing your data for RAG . ‘ Day 2 ’ problems exist around how to make systems work at scale , which can be a significantly bigger challenge .
126 May 2024