RAG vs CAG : Revolutionizing AI Efficiency and Speed If you’ve been keeping up with the latest buzz in generative AI, you’ve likely heard about how models like ChatGPT and GPT-4 are transforming fields such as content creation and customer support. However, while these models are incredibly powerful, they sometimes face challenges with factual accuracy or domain-specific knowledge. That’s where Retrieval-Augmented Generation (RAG) and Cache-Augmented Generation (CAG) come into play. Let’s explore these two innovative approaches and see how CAG might be redefining AI efficiency. What is Retrieval-Augmented Generation (RAG)? RAG enhances the capabilities of AI by allowing it to fetch real-time information from external sources, such as Wikipedia, research papers, or internal company documents. Think of it as giving your AI a dynamic memory boost. Instead of relying solely on pre-trained knowledge, RAG retrieves the most relevant documents in real time to ensure accurate and up-to...
When comparing various deep learning frameworks, it’s evident that TensorFlow stands out as the preferred choice among academics, businesses, and developers. GitHub activity for different ML frameworks( Source ) What is TensorFlow? TensorFlow is an open-source software library designed for machine learning and artificial intelligence. While it supports a variety of tasks, it is particularly well-suited for training and inference of deep neural networks. Alongside PyTorch, TensorFlow is one of the two most widely used deep learning libraries. Developed by Google Brain for internal research and production, the first version was released under the Apache License 2.0 in 2015. Google later introduced TensorFlow 2.0 in September 2019. TensorFlow supports multiple programming languages, including Python, JavaScript, C++, and Java, making it versatile for various applications across different industries. EXPLAIN TENSORFLOW What is a Tensor? A tensor is an n-dimensional vector or mat...