When working with large language models (LLMs), handling context chunks efficiently is crucial. But what happens when you’re dealing with 50+ context chunks in the post-retrieval process? It can get overwhelming, to say the least.
In this article, we’ll explore the best practices for managing multiple context chunks, ensuring your LLMs think more effectively after retrieval. We’ll dive into strategies for optimizing your processing pipeline, avoiding information overload, and streamlining your workflow.
Whether you’re a researcher, developer, or simply an AI enthusiast, this guide will help you navigate the complexities of post-retrieval processing and unlock the full potential of your LLMs.