In Retrieval-Augmented Generation (RAG), accurate and relevant information retrieval is crucial for generating high-quality responses. However, traditional retrieval methods often return results that are not optimally ranked for relevance. This is where **reranking** comes into play, significantly improving retrieval system performance.
Embedding Model converts texts, words, images into numerical form known as vectors, Vectors are used for Context and Relationships between texts, words, they are stored in Vector Database.
The effectiveness of a RAG system heavily depends on one fundamental preprocessing step: chunking.
In a hyper-competitive world where every moment counts, businesses are turning to AI and cloud technologies to deliver faster, smarter, and more personalized experiences.
What comes to mind when you hear "generative AI"? Probably ChatGPT crafting a quick reply or drafting an email, right?
