Shelf Blog
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Knowledge management (KM) is changing dramatically due to its role in supporting artificial intelligence (AI) solutions. It’s never been a better time to attend knowledge management conferences and get a sense of how KM is changing across industries. Here’s a breakdown of the knowledge...
How is generative AI used in enterprises? Visualize a daunting scenario: An intelligent system that can compose music, generate compelling articles, or write code based on learned data patterns is rapidly becoming a reality. With generative AI fundamentally changing the game, enterprises that...
Businesses have long been increasingly inundated with an unprecedented volume of data. The challenge now is not just about storing ample data but managing, classifying, and transforming this structured and unstructured data into fuel for the engine of business. The critical role of data...
A knowledge graph is a structure that connects diverse pieces of information, helping you uncover relationships and insights that might not be immediately apparent. In some cases, you need to bridge two types of data together: structured and unstructured data. In this article, we provide a clear,...
ChatGPT and other generative AI platforms are transforming how we access information, answer questions, and even create and interpret art. But there are limitations when it comes to performing more complex tasks like planning an event, or chaining multiple steps together like conducting buyer...
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...
At the core of human cognition is the concept of “attention,” a mechanism that allows us to focus on particular elements of our environment while filtering out others. This concept has inspired a transformative feature in deep learning models: the attention mechanism. By emulating the way humans...
Whenever you interact with a large language model (LLM), the model’s output is only as good as your input. If you offer the AI a poor prompt, you’ll limit the quality of its response. So it’s important to understand zero-shot and few-shot prompting as you can use these techniques to get better...
As the deployment of Large Language Models (LLMs) continues to expand across sectors such as healthcare, banking, education, and retail, the need to understand and effectively evaluate their capabilities grows with each new application. Solid LLM evaluation metrics for assessing output quality are...
A data pipeline is a set of processes and tools for collecting, transforming, transporting, and enriching data from various sources. Data pipelines control the flow of data from source through transformation and processing components to the data’s final storage location. Types of Data Pipelines AI...
Large language models have an impressive ability to generate human-like content, but they also run the risk of generating confusing or inaccurate responses. In some cases, LLM responses can be harmful, biased, or even nonsensical. The cause? Poor data quality. According to a poll of IT leaders by...
What is Retrieval-Augmented Generation? Retrieval-Augmented Generation (RAG) is a Generative AI (GenAI) implementation technique that is accelerating the adoption of GenAI and Large Language Models (LLMs) across enterprise environments. By enabling organizations to use their proprietary data in...