Shelf Blog
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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 list of knowledge management tools ideally designed to help an organization find and manage helpful information can be overwhelming. You probably can think of a handful of active software subscriptions in your company with some kind knowledge management component; these tools may be siloed...
Many organizations are exploring knowledge management because a proper knowledge management system is integral to a successful artificial intelligence strategy. If your organization has hundreds of files or decades of stored data and your own workforce can’t navigate it, then an artificial intelligence (or large language model) won’t be able to either.
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...
A data lakehouse is a modern data management architecture that’s designed to handle diverse data types and support advanced analytics. It’s a valuable tool for data scientists, project managers, AI professionals, and organizations that rely on data-driven decision-making. As businesses...
When it comes to data quality, unstructured data is a challenge. It often lacks the consistency and organization needed for effective analysis. This creates a pressing need to address data quality issues that can hinder your ability to leverage this data for decision-making and innovation. As you...
Choosing the right data format can significantly impact how well you manage and analyze your data, especially in big data environments. Parquet, a columnar storage format, has gained traction as a go-to solution for organizations that require high performance and scalability. Parquet offers...
The ability to manage, store, and analyze vast amounts of data is critical to your organization’s success. As you generate more data from diverse sources, you must choose the right infrastructure to handle this information efficiently. Two of the most popular solutions are data lakes and...