Shelf Blog
Get weekly updates on best practices, trends, and news surrounding knowledge management, AI and customer service innovation.
We encounter content chunks every day. Think of a recipe. A recipe contains content components like a title, a list of ingredients, cooking times, pictures of food, and instructions that contain individual steps. These are “chunks” that together compose a recipe. The process of chunking this...
5-Point RAG Strategy Guide to Prevent Hallucinations & Bad Answers This guide designed to help teams working on GenAI Initiatives gives you five actionable strategies for RAG pipelines that will improve answer quality and prevent hallucinations.
Natural Language Processing (NLP) is an interdisciplinary field blending computer science, artificial intelligence, and linguistics, aimed at enabling computers to understand, interpret, and engage with human language in both written and spoken forms. NLP combines computational linguistics with...
We live in an on-demand world. Customers expect every product delivered to the doorstep, every service ordered online, and any information they seek immediately available at their fingertips. When customers have questions, they expect answers–on demand. As customer expectations have risen, so have...
The field of Natural Language Processing (NLP) has witnessed significant advancements, yet it continues to face notable challenges and considerations. These obstacles not only highlight the complexity of human language but also underscore the need for careful and responsible development of NLP...
This article presents 10 Generative AI prompts tested and refined to improve customer service effectiveness excerpted from our eBook, 51 Tried and Tested Generative AI Prompts for Customer Service Agents. With Generative AI, agents can rapidly generate personalized, context-aware responses across...
Can we really trust Artificial Intelligence? Let’s face it. AI has trust issues. AI is rapidly permeating our lives. But perhaps even more rapidly permeating, are fears about AI. Fears that are largely due to a lack of transparency as to how AI works. These concerns are evident in questions people...
What Is Bias in AI? In the realm of artificial intelligence (AI), bias is an anomaly that skews outcomes, often reflecting societal inequities. AI bias can originate from various sources, including the data used to train AI models, the design of algorithms themselves, and the way results are...
Businesses have long been increasingly inundated with an unprecedented volume of data. The challenge now is not just about storing ample data but managing, classifying, and transforming this structured and unstructured data into fuel for the engine of business. The critical role of data...
Identifying how Generative AI and data preparation fits into your business case is a complex endeavor. If you are feeling overwhelmed trying to keep up with emerging AI technologies and applications — and it’s almost 100% likely that you are—you are not alone. Because “almost 100%” by definition...
Continuous Improvement and Machine Learning Ops (MLOps)The effectiveness of AI implementations, such as generative AI, is intrinsically linked to the quality and structure of the underlying data. However, maintaining the relevance and quality of this data is not a one-time task. It requires a continuous improvement approach, where machine learning...
In machine learning, embeddings are a technique used to represent complex, high-dimensional data like words, sentences, or even entire documents in a more manageable, lower-dimensional space. An analogy would be nice. Right. Think about Lego bricks. A lot of them. High-dimensional data is like the...