Most people show AI what good looks like. Few show it what bad looks like. This week's Prompt Lab technique: the negative example. When you give a model only positive examples, it learns the surface — length, structure, tone. It does not learn your boundaries. So it drifts toward the average of everything it has seen, which is rarely what a client-ready document looks like. Add one deliberately bad example with a short note on why it fails: "This version is too promotional. This one buries the decision in paragraph four." The model now has edges to steer between, not just a target to imitate. The effect shows up fastest in recurring outputs — status summaries, meeting notes, stakeholder updates — where "almost right" costs you ten minutes of editing every single time. Before: three revision rounds to remove filler. After: the filler never appears, because you showed the model what filler looks like. One good example plus one annotated bad example beats five good examples. Which recurri

Getting started with AI doesn't need a big budget or a tech team. Five low-risk steps to help a small business try AI sensibly and see what actually works.

Opdateret juni 2026 Få regler har skabt lige så meget usikkerhed blandt danske virksomhedsledere som EU's AI-forordning. Skal vi gøre noget? Gælder det også os? Og hvornår? Svarene er blevet sværere at finde i 2026, fordi reglerne netop er blevet ændret. Den gode nyhed er, at de færreste små og mellemstore virksomheder bygger den type AI, som forordningen rammer hårdest. Den mindre gode er, at "det gælder nok ikke os" ikke holder som strategi. Flere krav er allerede trådt i kraft, og de gælder også for virksomheder, der blot bruger AI-værktøjer udviklet af andre. Denne artikel giver dig overblikket i klar tale: hvad forordningen er, hvilke regler der faktisk gælder lige nu, hvad der blev udskudt i 2026, og hvad din virksomhed med fordel kan gøre allerede i dag. Hvad er EU's AI-forordning — kort fortalt EU's AI-forordning er verdens første samlede lovgivning om kunstig intelligens. Den trådte i kraft den 1. august 2024 og rulles ud i etaper frem mod 2027, så virksomhederne får tid til a

Asking a model to "write it well" produces average work. Asking it to write, then attack its own draft, does not. This week's Prompt Lab technique: the two-pass pattern. Pass one: generate the draft as usual. Pass two, in a new message: "Review this draft as a skeptical [CFO / procurement lead/regulator]. List the three weakest points and what they would push back on. Then revise." The reason this works is structural. A model drafting and judging in the same breath optimizes for coherence, not quality — it will not undermine the sentence it just wrote. Separating the two roles forces an actual evaluation step, just as no serious organization lets the author approve their own document. The reviewer persona matters more than people expect. "Review this" gives you grammar notes. "Review this as the person who has to defend it to the board" gives you substance. Before: a draft that reads well. After: a draft that survives the meeting. Which reviewer persona would expose the most in your ty

If you have searched for the best LLM for business, you have probably found a dozen confident, contradictory answers. Each article crowns a different winner. Each provider points to its own model. None of them is lying, exactly — they are answering a question that has no single answer. There is no best LLM for business, any more than there is a best vehicle for a business. The right choice for a delivery firm is wrong for a taxi company. Models are the same: each is strong at some jobs and ordinary at others, and the one that fits your work depends entirely on what that work is. This article explains why the question misleads, what "best" should actually mean for your company, and how to find the model that fits — without chasing whichever name tops this month's leaderboard. Why "best LLM for business" is the wrong question The question assumes a single ranking exists. It does not. Model performance is multidimensional, and the dimensions pull against each other. A model that writes fl

Self-hosted AI vs cloud AI: compare the real costs, data control and scaling trade-offs, and learn which model fits your company before you commit.