Data Segmentation Strategies
Understanding how to effectively segment data is crucial for solving complex questions. By identifying capabilities and inventory issues, one can determine whether to enhance existing datasets or integrate new data sources. This approach not only improves retrieval mechanisms but also ensures that the right information is available for accurate responses.In this clip
From this podcast
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Why Your RAG Pipeline Is Broken, and How to Fix It with Jason Liu - 709
Related Questions
How do you come up with a data strategy?
I'm thinking that classical or deep ML solutions have a flaw in that they cannot be extended. For example, if one builds a model and wants to introduce a new feature, typically the model has to be retrained from scratch. So, I'm considering building a Knowledge Graph using LLMs. This knowledge graph would have to include time-dependent data (for example, it should be able to retrieve the "current" President of the USA and also previous presidents if asked). I'm thinking this Knowledge Graph could be used for Retrieval Augmented Generation (RAG) to help with business goals or maybe used with more clever User Interfaces (UIs). I'm not sure how to build or populate this KG and also have a rough idea of how to use it. Can you help me with suggesting particular paths for building and populating this Knowledge Graph?