Shelf Blog
Get weekly updates on best practices, trends, and news surrounding knowledge management, AI and customer service innovation.
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....
When it comes to building a knowledge platform, two giants enter the fray: SharePoint vs. Shelf. Business owners read reviews about one and the other, but still can’t figure out which is the better choice. SharePoint was designed for storing and sharing files. Shelf was designed to make knowledge...
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...
Nearly 80% of corporate data is unstructured. It can be found anywhere: in documents, emails, support tickets, call transcripts, and so on. This data accumulates over time – one year, two years, five years, and so on. But now, enterprise AI promises to turn this massive amount of data into...
When an AI agent invents a return policy that doesn’t exist or refers to a contract clause that never existed, these are AI hallucinations. In a consumer chatbot, this is simply annoying. In an enterprise agent that responds to customers or makes operational decisions, however, it poses a massive...
Once again, we’ll be exploring the topic of AI agents because more often we see phrases like “the AI agent isn’t working” or “the AI agent broke down”. Let’s be honest: most of the time, they break down or don’t work properly not because you chose a bad model, but because they’re running on...
Many people mistakenly believe that knowledge management is some system you can simply buy and turn on. But in reality, it is a complex, continuous, cyclical process: capture knowledge, organize it, store it, share it, use it, update it, and repeat the cycle. However, if even one step is skipped...
A traditional knowledge management system operates on a single principle: store and wait. One person creates an article, another searches for it using keywords and the article may or may not surface . AI-based knowledge management systems work differently. Unlike traditional systems, it doesn’t...