IntroductionRetrieval-Augmented Generation, commonly known as RAG, has been making waves in the realm of Natural Language Processing (NLP). At its core, RAG is a hybrid framework that integrates retrieval models and generative models to produce text that is not only contextually accurate but also information-rich. What is RAG?Retrieval-Augmented Generation (RAG) is the process of optimizing […]
Category Archives: Large Language Models (LLM)
An LLM is a specialized type of artificial intelligence (AI) that has been trained on vast amounts of text to understand existing content and generate original content. These models are deep learning algorithms that can perform a variety of NLP tasks. They use transformer models and are trained using massive datasets. This enables them to recognize, translate, predict, or generate text or other content.
Vector embeddings are a powerful technique for representing complex, unstructured data in a way that preserves their semantic meaning and enables efficient processing by machine learning algorithms. In this blog post, we will explore what vector embeddings are, why they are useful, and how they are created and used for various tasks such as text […]
Large language models (LLMs) are neural network models that can generate natural language texts based on some input, such as a prompt, a query, or a context. LLMs have shown impressive results in various natural language processing tasks, such as text summarization, machine translation, question answering, and code generation. However, building applications that use LLMs […]