Shelf Blog
Get weekly updates on best practices, trends, and news surrounding knowledge management, AI and customer service innovation.
The Rush to Deploy Generative AI Nowadays, organizations across industries are scrambling to deploy generative AI. While some have already implemented generative AI projects into production at a small scale, many more are still in the proof-of-concept phase, testing out different use cases. A...
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating an external retrieval system. This allows the AI to ground responses in authoritative, real-world data, which mitigates hallucinations and extends an LLM’s knowledge base beyond its pre-training data. ...
AI models don’t think—they predict. When they generate false or misleading outputs, it’s because they’re filling in gaps based on patterns in their training data. This phenomenon, known as AI hallucination, leads to responses that sound correct but have no basis in reality. For AI leaders...
Making sure your data is ready for AI agents is critical for the success of your projects. As an AI leader or tech strategist, you understand the importance of data accuracy and integrity in AI models. Well-prepared data leads to more reliable outcomes, higher customer satisfaction, and better...
As enterprises race to integrate AI agents into operations, many are discovering a hard truth: it’s not the models holding them back—it’s the mess. Specifically, the mess of unstructured data. While much of the excitement in enterprise AI focuses on models, tools, and interfaces, one fundamental...
Why Good Data is the Secret Ingredient for AI Success The Real Cost of Bad Data in AI AI performance metrics often look straightforward: systems should respond within 3 seconds, successfully complete 85% of tasks, and keep error rates below 5%. But these numbers lose all meaning if the AI is...
Prevent Agent Failure Before It Happens. In today’s data-driven world, AI agents are crucial for maintaining a competitive edge. However, many organizations are unknowingly undermining their AI’s potential due to poor data quality. This article addresses the critical issues of data...
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...
The world’s leading AI companies—OpenAI, Google, and Microsoft—are redefining what’s possible with enterprise AI. If your business is relying on a single-agent AI setup, you might be missing out on its full potential. Multi-agent AI systems take things to the next level. Unlike...
Incorporating AI agents into your operations can be a game-changer, offering unparalleled scalability, efficiency, and optimization. However, without a well-thought-out strategy, AI can quickly become a bottleneck rather than a solution. To ensure a smooth and effective implementation, it’s...
Google’s Bard chatbot made news with a major error. It wrongly stated that the James Webb Space Telescope captured the first photos of exoplanets. This incident showed how AI hallucinations can spread false information through even the most advanced systems. These AI mistakes aren’t...
Knowledge management skills are essential for building intelligent, AI-ready knowledge ecosystems. As organizations rely on AI for decision-making, you need the right expertise to structure, manage, and optimize information flows. This guide covers the core skills, certifications, and...