Shelf Blog

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...

Implementing a knowledge management system or exploring your knowledge strategy? Before you begin, it’s vital to understand the different types of knowledge so you can plan to capture it, manage it, and ultimately share this valuable information with others. Populating any type of knowledge base...

Data decay is the gradual loss of data quality over time, leading to inaccurate information that can undermine AI-driven decision-making and operational efficiency. Understanding the different types of data decay, how it differs from similar concepts like data entropy and data drift, and the...

Retrieval-augmented generation (RAG) is an innovative technique in natural language processing that combines the power of retrieval-based methods with the generative capabilities of large language models. By integrating real-time, relevant information from various sources into the generation...

A data mesh is a modern approach to data architecture that decentralizes data ownership and management, thus allowing domain-specific teams to handle their own data products. This shift is a critical one for organizations dealing with complex, large-scale data environments – it can enhance...

The terms “data science” and “data analytics” are often used interchangeably, but they represent distinct fields with different goals, processes, and skill sets. Understanding the differences between these two disciplines is crucial for professionals who work with data, as...