Shelf Blog
Get weekly updates on best practices, trends, and news surrounding knowledge management, AI and customer service innovation.
Not every knowledge management system serves the same purpose. And that makes perfect sense, since some are designed to store documents, while others are designed to help customers find answers on their own. Lumping them all under the single term “knowledge management system” obscures the...
We’re convinced that absolutely every large company has a massive amount of data, but when it’s actually needed, no one can find it. CRM systems store customer history, ERP systems store financial transactions, document systems store contracts and regulations, and email contains agreements that...
Imagine a customer call that starts with a simple question about a return. What does your agent do? Most likely, they spend several minutes scrolling through tabs in search of the right document. But what’s happening to the customer in the meantime? They hear a pause, perhaps some pleasant music...
Every enterprise possesses vast amounts of knowledge yet spends enormous effort maintaining it manually. Some people tag documents; others update articles that became outdated a month ago; still others answer the same questions over and over again because the answer exists but is hard to find....
When a company implements enterprise RAG, it has perfectly understandable expectations that the AI will provide accurate and up-to-date answers based on corporate knowledge. But instead, the company sees confident yet incorrect answers, inconsistencies across interactions, and reliance on outdated...
Do you know the two main ways to incorporate your company’s knowledge into a language model? You can either retrieve it on-demand (RAG) or “bake” it directly into the model (fine-tuning). But if you make the wrong choice, you’ll end up with a bloated budget, outdated answers, or the need to...
The development of artificial intelligence has not only added new features to knowledge management; it has, in fact, transformed the very purpose of these tools. Previously, such tools stored and organized documents for people. But their new purpose is to prepare knowledge so that AI agents can...
Despite the rapid development and adoption of artificial intelligence, not all knowledge management platforms are AI-ready. Most were designed to store and organize documents, but specifically for human use. When AI agents or Copilots are directed to them, they access everything inside:...
SharePoint is one of the most popular document storage platforms for enterprise companies. And given its popularity, building a knowledge base around it seems like an obvious step. And indeed, it works well for storing and sharing articles. However, problems arise the moment GenAI is directed to...
Context engineering is the practice of designing and managing the complete information environment that AI agents use to reason and act. Unlike prompt engineering, which optimizes a single input prompt, context engineering builds a complete system of knowledge, memory, tools, and governance that...
Large language models are confident, eloquent, and often get questions about your business wrong. The reason is that they weren’t trained on your data. They know the world in general, but they don’t know your return policy, your product specifications, or the current terms of your contracts....
Most enterprise companies don’t have a knowledge problem. They just have a problem accessing that knowledge. This is a mistake most companies make: you know the answers exist, but they’re scattered all over the place. When a new employee joins your team, they don’t know where to look. A support...