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Articles by Tag: LLM

No Clicks? No Problem! How to Stay Visible in the Zero-Click Search Age
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No Clicks? No Problem! How to Stay Visible in the Zero-Click Search Age

Zero-click search now acts as the main web search method, serving users instant answers without a single site visit. Businesses can no longer rely only on SEO for effective online visibility. Marketing managers, along with web developers and content creators, must understand zero-click search dynamics to preserve visibility and digital value.
Model Context Protocol: Simplifying AI Connectivity for Enterprises
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Model Context Protocol: Simplifying AI Connectivity for Enterprises

The Model Context Protocol (MCP) is an open standard developed by Anthropic for communication between AI models and data sources. It eliminates the need for developers to build custom connections for each new data source, tool, and API. AI developers can look to MCP to simplify development and improve interoperability for their AI systems.
Unraveling the Local Loop: A Guide to Safer Locally Deployed AI
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Unraveling the Local Loop: A Guide to Safer Locally Deployed AI

Organizations are increasingly adopting large language models (LLMs) to enhance operations and decision-making. While deploying these models locally offers significant advantages in terms of data sovereignty and control, it also presents unique security challenges that cannot be overlooked. IT executives who have, or are planning, a local LLM deployment should make sure it is implemented securely, ethically, and effectively to avoid data breaches and operational risks.
Say Hello to Your New AI-Language Tutor
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Say Hello to Your New AI-Language Tutor

Stanford University's Tutor CoPilot has improved students’ mathematics skills by up to 9% over two months. AI’s benefits also extend to language learning courses in educational institutions. IT leaders in education institutions can use open-source tools to create applications to save on costs and protect student and staff data.
Small Enterprises, Big AI: How to Use LLMs to Stop Financial Fraud
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Small Enterprises, Big AI: How to Use LLMs to Stop Financial Fraud

Traditional fraud detection methods are resource-intensive and difficult to implement and maintain. Leveraging large language models (LLMs) offers a more efficient approach. LLMs can analyze vast amounts of data in real time, identifying fraud with less complexity. CIOs at SMEs should consider incorporating LLMs into their fraud detection systems to strengthen security while simplifying operations.
The LLM Takeover: AI For All
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The LLM Takeover: AI For All

AI is becoming a necessary software feature for vendors to stay relevant and ahead of their competition. One major issue with AI in software is the trust that your business data is private and protected. Without this trust, your data could be used by your software vendor or third parties to train their AI models. This article discusses how to manage software with AI to protect your data.
Just Cache It (Part 2): Prompt Caching vs RAG
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Just Cache It (Part 2): Prompt Caching vs RAG

Businesses are continuing to enhance their efficiency by using AI. This increases the need for LLMs that perform well on enterprise tasks. Fine-tuning is not a viable method because it is costly. Prompt caching (context caching) and Retrieval-Augmented Generation (RAG) are more suitable. AI engineers should read this article to learn more about these two methods to create cost-effective LLMs that perform well on their enterprise data.
Just Cache It (Part 1): Maintaining Context with APIs and LLMs
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Just Cache It (Part 1): Maintaining Context with APIs and LLMs

It has become easier to create AI applications due to the ease of integration by using APIs. High cost is one challenge when frequent API calls are made to LLMs with similar content to add context. Prompt caching, or context caching, creates a cache to solve this challenge. AI engineers must use prompt caching to decrease inference fees and reduce latency.
EU Regulations, Technologies, AI Realities and Cyber Risks: 2024 Tech Insights
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EU Regulations, Technologies, AI Realities and Cyber Risks: 2024 Tech Insights

2024 saw significant shifts in technology, with the EU's AI Act and DMA impacting businesses alongside the rise of modular laptops and the persistent threat of cyber attacks. This review highlights some of the developments that interested IT leaders. This list suggests CIOs and IT executives should continue to prioritise compliance, evaluate new technologies, and strengthen cybersecurity in 2025.
Navigate the Technology Trends of 2025 – Compliance
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Navigate the Technology Trends of 2025 – Compliance

From the EU AI Act to emerging state-level AI laws in the US, 2025 promises heightened scrutiny and demands on IT systems. Organizations must adopt forward-thinking strategies, leveraging emerging technologies like LLMs and governance tools, to navigate this terrain effectively. CIOs should prioritise proactive compliance measures to safeguard operations and maintain competitive advantage.