RAG and Parent Document Retrievers: Making Sense of Complex Contexts with Code
Introduction
Retrieval-Augmented Generation (RAG) is an intricate field brimming with subtleties, one of the most intriguing being the ‘parent document retriever’. As we wade deeper into this technical domain, we’ll demystify the interplay between Large Language Models (LLMs) and embeddings, drawing inspiration from an earnings call by Google. Let’s decode the systems driving adept document retrieval and insightful information extraction.
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Context in LLMs: Beyond Simple Interpretations
The prowess of LLMs lies in their ability to sift through mountains of data to identify relevant nuggets of information. However, their efficiency hinges on the model’s quality. Bombarding them with excessive data can lead to diminished precision, which underscores the importance of RAG in filtering and refining data for LLMs.
