Shelf Blog
Get weekly updates on best practices, trends, and news surrounding knowledge management, AI and customer service innovation.
Challenges and Considerations in Natural Language Processing
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...
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.
10 Tried and Tested Generative AI Prompts for Customer Service Agents
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...
Opening the Black Box: How to Create AI Transparency and Explainability to Build Trust
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...
AI Bias, What It Is and How to Fix It
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...
The IT Leader’s Guide to Preparing Structured and Unstructured Data for Generative AI
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...
Generative AI and Data Preparation in an Integrated AI Strategy
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...
What Are Embeddings in 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...
The Evolution of AI: Introducing Autonomous AI Agents
ChatGPT and other generative AI platforms are transforming how we access information, answer questions, and even create and interpret art. But there are limitations when it comes to performing more complex tasks like planning an event, or chaining multiple steps together like conducting buyer...
What are Neural Networks and How Do They Work With Generative AI
Neural networks involve a series of algorithms designed to recognize patterns, interpret data, and make decisions or predictions. They are modeled loosely after the human brain’s architecture. Neural networks have become a cornerstone of AI technologies alongside others, such as rule-based...
Fine-tuning Large Language Models for AI Accuracy and Effectiveness
We need more than just artificial intelligence. We need virtual experts that are accurate, authoritative, and effective. It’s not enough to deploy AI technologies to answer customer service questions, assist a doctor’s medical diagnosis, identify a negotiator’s key clauses in a contract, provide...