26 posts
The field stopped asking 'what prompt should I write' and started asking 'what should the model know at the moment it decides.' Same eighteen posts, one honest rename.
Six different problems get mistaken for each other constantly, and each one has a specific, usually cheaper fix than the one people reach for first.
A perfect prompt run with no context, no memory, no evaluation, and no guardrails still fails in production, for reasons that have nothing to do with its wording.
AI can write you a working PowerShell script in seconds. Whether it's safe to run against your tenant is a completely separate question, with its own workflow.
Not another list of coding prompts. The actual engineering loop, requirement through revision, that decides whether AI-generated code survives contact with a real codebase.
Retrieval can hand the model the exact right document and still get a wrong answer, if the prompt around that document doesn't do its half of the job.
A tool description is a prompt the model reads to decide whether to use it at all. Get that wrong and no amount of clever wording elsewhere fixes it.
Every AI response is assembled from up to seven layers, written by different parties with different trust levels. Here's what each one actually controls.
Not every piece of text you paste into a prompt is safe to treat as an instruction. Where that trust breaks down, and how to structure a prompt so it doesn't.
A prompt edited live in a vendor dashboard has no history, no diff, and no way back. Git, change history, and rollback fix all three.
Reading one better-looking answer isn't evaluation. A real testing framework compares prompt versions against a fixed dataset, with real, repeatable metrics.
Six ingredients, combined into one prompt, that work for almost any task. Not another list of prompts to copy, the actual structure they're all built from.
Asking for 'a table' or 'JSON' isn't a format specification, it's a suggestion. Here's what actually gets consistent, parseable output every time.
Three versions of the same request, short and vague, long and structured, and long with real information, tested against each other instead of argued about.
One giant prompt asks a model to research, analyse, validate, and generate all at once, with no chance to catch a mistake before it reaches the final answer. Chaining fixes that.
The AI didn't get worse. The prompt was vague. Here's the actual difference between a request that gets a generic answer and one that gets a genuinely useful one.
Six ingredients, written once as a template with variables, instead of retyped from memory every time. This is where prompt engineering starts becoming engineering.
No magic phrase can supply a fact the model was never given. The real lever most people underuse isn't wording, it's what information actually reaches the model.
Examples aren't free, they cost tokens and effort to write. Here's the actual decision behind when that cost is worth it, and when it just adds noise.
AI hallucinations aren't a bug you can patch, they're built into how generative AI works. Here's why they happen, what they cost, and the concrete techniques that reduce the risk.
AI tools can get expensive fast if you are not careful. This practical guide shows IT admins and teams how to use fewer tokens, choose the right model for each task, set hard spend caps, and manage licences, so you get maximum value from every penny you spend on AI.
Three major AI tools, one IT admin, and months of real testing. This is an honest comparison of ChatGPT, Google Gemini, and Microsoft Copilot for the tasks IT professionals actually do: scripting, troubleshooting, documentation, and more.
Most people ask ChatGPT to write PowerShell the wrong way and get generic, untested code. This guide shows the exact prompt structure I use to get production-quality scripts first time, including the four elements that make the difference.
A system prompt is the single biggest upgrade you can make to your AI setup. It tells the AI who it is, how to behave, and what to focus on, before you even ask a question. Here is how to write one that transforms your results.
Never studied computers? No problem. This guide explains Artificial Intelligence, Generative AI, and ChatGPT in plain everyday language, with real-life examples anyone can understand, and a clear step-by-step path to start learning AI from scratch.
Move beyond toy demos to production-ready LLM integration: structured outputs, few-shot prompting, prompt chaining, cost control, and evaluation frameworks that actually work.
