AI Governance

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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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Decision intelligence: Why insights alone don’t create value

Most organizations are struggling to use AI insights. Even as it’s been easier than ever to produce predictions, recommendations and scores, many data science and business teams end up with a stockpile of unused information that doesn’t drive meaningful transformation. Decision intelligence helps organizations bridge that gap by embedding insights […] The post Decision intelligence:

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QA Engineer

Testing Beyond Pass or Fail: A QA Engineer’s Lessons from IMDA’s LLM Testing Starter Kit at Spritle

I’ve been in QA for a few years now. I know how testing works. You write a test case. You define the expected result. You run it. It either passes or fails. Simple. So when our team started working on an AI-powered feature, I thought, okay, same process. Different kind of input, but same idea.

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Modernizing attendance ticketing in SAS Viya using SAS Agentic AI Accelerator

Learn how the SAS Agentic AI Accelerator and SAS Viya can be used to build a governed, multi-agent support-ticket solution that combines text analytics, RAG, LLMs, business rules, and human oversight to improve resolution speed, accuracy, and operational efficiency. The post Modernizing attendance ticketing in SAS Viya using SAS Agentic AI Accelerator appeared first on

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Why AI Pilots Fail to Scale in Enterprises

Why it matters: Most enterprise AI pilots never reach production. Here is why they stall and the data, governance and change moves that let the rare ones scale.

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IMDA

Lessons from IMDA’s LLM Testing Starter Kit: An AI Assurance Perspective

Quality Assurance has always been about understanding risk and validating systems before they reach production. After more than eight years in QA and now working in AI security, governance, and red teaming, I often compare traditional testing practices with the challenges introduced by AI systems. While the risks have evolved from software defects to hallucinations,

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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

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

Predictive Analytics in EduTech Through an AI-Driven Software Development Lifecycle

Student retention has become a board-level metric for universities, bootcamps, and enterprise learning platforms. Yet many EduTech companies still struggle with fragmented LMS data, unreliable adaptive models, and FERPA compliance issues that slow releases and increase risk. This is where ADLC changes the conversation. An AI-driven software development lifecycle gives EduTech teams a structured framework

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