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Articles by Tag: Generative AI

AI Bill of Materials (AIBOM) - Strengthening AI Integrity and Transparency
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AI Bill of Materials (AIBOM) - Strengthening AI Integrity and Transparency

The widespread adoption of Generative AI (GenAI) in applications offers substantial advantages but also introduces various threats because of the myriad components they comprise. To ensure the integrity of AI/ML systems, organizations should manage every component through an AI Bill of Materials (AIBOM) to inventory the data, models, and infrastructure used. Developers, data scientists, and security experts should advance their AI maturity by adopting AIBOMs to secure and optimize their AI systems.
Sparsity: A Crash Diet to Reduce Model Size and Latency
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Sparsity: A Crash Diet to Reduce Model Size and Latency

State-of-the-art AI models require powerful hardware to run and they have enormous file sizes–leading to high hosting and inference costs. SMEs are unable to integrate these models into their applications because of their limited budget. Sparsity is a technique that prunes a model’s parameters leading to a smaller, faster model that can run on-device. An SME’s IT team can use sparsity to integrate powerful models into their applications while reducing hosting and inference costs.
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.
Navigate the Technology Trends of 2025 – Artificial Intelligence
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Navigate the Technology Trends of 2025 – Artificial Intelligence

The new year brings more challenges and opportunities for CIOs and IT executives. Knowing what they are and how to meet them is crucial for enterprises to excel in their respective markets. This four-part series identifies the four major trends IT leaders must navigate in 2025–the first is Artificial Intelligence (AI).
AI-Driven Enterprise Search: What IT Leaders Need to Understand
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AI-Driven Enterprise Search: What IT Leaders Need to Understand

Traditional web searches can be irritating to employees who must use complex queries because the system does not understand what they want. AI-powered search (or an AI search engine) mitigates these issues by understanding context while providing summaries, conversation functionality, and citing sources. IT leaders should read this article to understand how AI search engines work and how choose the best solution for their needs.
Navigate Regulations with LLM-Assisted Compliance Strategies
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Navigate Regulations with LLM-Assisted Compliance Strategies

The increase in regulatory requirements, such as the European Union AI Act, the General Data Protection Regulation (GDPR) and others, heralds an era of increased complexity and scrutiny. This has seen SMEs face challenges in implementing robust compliance strategies to address the myriad of tech regulations and requirements. Large Language Models (LLMs) have been seen as a viable option to assist with the complex nature of these requirements. Tech leaders and compliance officers should understand how they can use this emerging technology to enhance their regulatory compliance.
Avoid AI Chatbot Failures with an Effective Deployment Strategy
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Avoid AI Chatbot Failures with an Effective Deployment Strategy

AI chatbots are useful tools to deploy on websites to assist customers. Benefits include boosting user experience, making websites more friendly, and reducing the cost of support staff. Despite all of the good, AI chatbots can do more harm than expected. Web development teams and UX designers must understand these dangers to create a successful AI chatbot deployment strategy.
Limitations Unveiled: Exploring the Restrictions of Large Language Models
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Limitations Unveiled: Exploring the Restrictions of Large Language Models

This article dives into the burdens and constraints of using LLMs for key operational and strategic tasks. It highlights key areas where LLMs can fall short and significantly impact business operations. Understand the limitations of LLM implementations so that you can make informed decisions and set realistic expectations of what is possible with these models.