Shelf Blog
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Artificial intelligence engines need data to learn and operate, but the data you and I find meaningful is foreign to machines. Machines need data translated to their preferred language: math. This conversion happens with the help of vectors. What are vectors in machine learning? Vectors are...
5-Point RAG Strategy Guide to Prevent Hallucinations & Bad Answers This guide designed to help teams working on GenAI Initiatives gives you five actionable strategies for RAG pipelines that will improve answer quality and prevent hallucinations.
Synthetic data is artificially generated data that imitates real-world data without using actual information. It is generated through algorithms and statistical techniques to capture the same patterns and characteristics found in real data. Instead of relying on sensitive or private data,...
Businesses face an overwhelming amount of data rich with insights to unlock. Traditional keyword-based searches have long been the go-to method for wading through this informational deluge, but as the complexity and volume of data expand, the inefficiencies of such systems become more apparent....
Data hydration, the process of enhancing and enriching existing data, has become increasingly vital in the era of big data and artificial intelligence. In this article, we explore the concept of data hydration, the challenges organizations face in managing structured and unstructured data, and the...
As artificial intelligence continues to push boundaries and redefine industries, Generative Adversarial Networks (GAN) have emerged as a game-changer. GANs are transforming the way we generate creative content, from artwork to music, and are revolutionizing fields such as healthcare, art,...
For the successful implementation of Artificial Intelligence (AI), the significance of properly integrating content metadata in AI with Large Language Models (LLMs) cannot be overstated. LLMs, such as Llama, Mistral and Claude, have become instrumental in a myriad of applications, ranging from...
Computers are great at understanding numbers, but not with words. In order to help machines understand, manipulate, and produce words, we have to convert those words to numbers using a process called text vectorization. Text vectorization is the process of turning words and documents into...
From its inception in the theoretical halls of mid-20th century academia to the sophisticated, AI-driven algorithms of today, the evolution of NLP has not only advanced our understanding of language processing but also pushed the frontiers of what’s possible, transforming how we interact...
We encounter content chunks every day. Think of a recipe. A recipe contains content components like a title, a list of ingredients, cooking times, pictures of food, and instructions that contain individual steps. These are “chunks” that together compose a recipe. The process of chunking this...
Natural Language Processing (NLP) is an interdisciplinary field blending computer science, artificial intelligence, and linguistics, aimed at enabling computers to understand, interpret, and engage with human language in both written and spoken forms. NLP combines computational linguistics with...
We live in an on-demand world. Customers expect every product delivered to the doorstep, every service ordered online, and any information they seek immediately available at their fingertips. When customers have questions, they expect answers–on demand. As customer expectations have risen, so have...