Discover how Large Language Models (LLMs) power Generative AI systems, driving intelligent automation, scalability, and enterprise transformation.
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.
From generating human-like text to automating customer support and assisting with research, LLMs are changing the way businesses and individuals access and process information.
