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IMDA

Beyond the Checklist: A Practitioner’s Review of IMDA’s LLM Testing Starter Kit

Introduction As large language models move from proof-of-concept into production systems that touch real users, real money, and real decisions, the industry has been crying out for structured, actionable guidance on how to test them responsibly. IMDA’s Starter Kit for Testing LLM-Based Applications is a meaningful answer to that call. It arrives at exactly the […]

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From Traditional QA to AI Assurance and Governance

Breaking the Black Box: From Traditional QA to AI Assurance and Governance

I recently had the opportunity to review IMDA’s Starter Kit for Testing LLM-Based Applications for Safety and Reliability. As someone who has spent over 14 years in Quality Assurance, I was curious to see how established testing principles are being adapted to address the unique challenges introduced by Large Language Models (LLMs). What I expected

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Breaking the Black Box: From Traditional QA to AI Assurance and Governance

Testing for Trust: What IMDA’s LLM Testing Starter Kit Teaches Us At Spritle About AI Assurance

Breaking the Black Box: From Traditional QA to AI Assurance and Governance I recently had the opportunity to review IMDA’s Starter Kit for Testing LLM-Based Applications for Safety and Reliability. As someone who has spent over 14 years in Quality Assurance, I was curious to see how established testing principles are being adapted to address

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ITSM maturity

Why ITSM Maturity Matters More Than Tool Choice

Introduction One of the most common mistakes businesses make in IT service management is believing that a new tool will automatically solve operational problems. A platform gets replaced. New automation features are introduced. Dashboards become more advanced. Expectations rise quickly. But a few months later, support teams still struggle with delayed resolutions, inconsistent workflows, and

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AI Code Generation Inside ADLC: How It Cuts Dev Time Without Cutting Quality

Introduction Development timelines are shrinking, but expectations are rising. US engineering teams are expected to ship faster, iterate more often, and still maintain production-grade quality. According to GitHub’s 2025 developer report, over 70% of teams now use some form of AI-assisted coding, yet many still struggle to translate that into real delivery speed. Here’s the

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Why IT Data Is Ignored in Leadership Meetings

Introduction Here’s the thing companies are generating more data than ever, yet a surprising amount of IT data never makes it into leadership conversations. Dashboards exist, reports are shared, and analytics tools are in place. Still, when decision-makers sit down, IT insights often take a backseat. This isn’t just a communication gap. It’s a missed

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AI Risk & Compliance in 2026: Why QA Teams Must Lead the Shift

Artificial Intelligence is no longer a futuristic concept or an experimental capability. In 2026, AI has firmly embedded itself into core business operations—powering decisions in hiring, finance, healthcare, customer experience, and beyond. This shift brings a fundamental change: AI risk is now business risk. For Quality Engineering teams, especially QA leaders, this marks a turning

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How Poor ITSM Impacts Employee Experience

In today’s digital workplace, technology is the backbone of productivity. Employees rely on IT systems for communication, collaboration, data access, and task management. This makes IT Service Management (ITSM) a critical function within every organization. ITSM refers to the structured processes and tools used to design, deliver, manage, and improve IT services. When ITSM operates

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Why IT Reports Don’t Make Sense to Business Leaders

Every month, IT teams walk into leadership meetings armed with detailed reports — uptime statistics, incident logs, SLA compliance charts, performance dashboards, and infrastructure analytics. The data is accurate.The effort is significant.The reporting is thorough. Yet when the presentation ends, many business leaders are left with a silent question: What does all of this actually

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The Rise of Multimodal AI Agents: Smarter Systems or a Bigger Risk?

Artificial intelligence is quietly undergoing one of its most important shifts yet. For years, AI agents were largely confined to text—answering questions, generating content, or automating simple, rule-based tasks. Useful, yes—but limited. That limitation is now disappearing. We’re entering the era of Multimodal AI Agents—systems that can see, hear, read, reason, and act across multiple

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