Dive into Chunking Strategies for RAG with Zain 💚

​One of the most promising use cases is Retrieval Augmented Generation (RAG), as it enables teams across all industries to leverage the power of LLMs with their own data. But it's one thing to develop a prototype - another to use RAG in production.

​To optimize your RAG applications you can think of RAG as a framework, where each portion of the R, A, and G pipeline can be improved, evaluated, and assessed!

​Join us for this online workshop to learn more about different chunking strategies in the context of RAG Applications and how to leverage them to optimize the results of your RAG system.

​What You Will Learn

​We dive into basic techniques like Character Splitting or Recursive Character Splitting and talk about some common challenges and considerations like the optimal selection of chunk size and overlap windows.

​Together we also explore semantic chunking techniques that dynamically adjust based on textual meaning and discuss the use of LLM-based chunking for automating chunk creation.

​To add up to all of this we discuss Small2Big, a method that uses different chunks for retrieval versus generation, and demonstrates how leveraging metadata from chunks can refine search results in RAG systems.

​We are looking forward to dive into this topic with you.

​In addition to a great hands-on experience, you also get answers to your questions!

​If you want to discuss more topics like this with other community members we love to invite you to our Community RAG Corner: https://weaviate.slack.com/archives/C07EJS6LQVA.