AI & Machine Learning

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Prompt Engineering as an Operating Model: Versioned Prompts, Evaluation, and Governance

TL;DR Prompt engineering becomes an enterprise operating model when prompts influence production behavior. Prompts need owners, versions, review gates, evaluation tests, deployment controls, monitoring, and rollback. A prompt that controls support answers, tool use, routing, security behavior, or customer communication should be treated like production logic, not a note in a shared document. Introduction Prompt

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Azure Local as a Digital Power Grid: A Practical Architecture for Distributed Infrastructure

TL;DR Azure Local is best understood as a distributed infrastructure platform governed through a common Azure control plane. The electrical grid metaphor works because applications, data, and compute remain close to the locations consuming them, while identity, policy, monitoring, security, and automation provide consistent operating standards across the estate. The metaphor also needs boundaries. Centralized

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AI Business Value Drift: When Model Quality Holds but the ROI Quietly Disappears

TL;DR AI ROI is a monitored condition, not a permanent project status. A production AI use case should be described as currently realized and certified only while its baseline, outcome, complete cost, quality, risk, and operating assumptions remain valid. An AI system can remain technically healthy while its business case deteriorates. Provider pricing can change.

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Designing a Shared VCF 9.1 Recovery Site for vSAN, VMFS, and NFS Workloads

TL;DR VCF 9.1 changes the economics and architecture of VMware disaster recovery by allowing virtual machines on vSAN, VMFS, and NFS datastores to replicate into a vSAN ESA target. It also supports fan-in designs where multiple source clusters use one centralized recovery site. The important design point is that a shared recovery site is not

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