Generative AI is now a practical part of search, content creation, and analytical workflows. Yet as usage grows, so does a familiar and expensive issue: answers that sound confident but are wrong. Often labeled “hallucinations,” the word suggests the AI system is broken. In reality, this behavior is usually predictable and stems from vague instructions – or, more precisely, vague prompts. Ask an AI for a “cookie recipe” and nothing else. You don’t mention allergies, taste preferences, or constraints. You might get Christmas cookies in July, a peanut-heavy recipe, or something so dull and generic it barely qualifies as a “sweet treat.” This lack of specificity leads to outputs that miss the mark. You should assume a model will go off track unless you proactively set clear boundaries. Rubrics are a powerful way to do this. Below, we’ll look at how rubric-based prompting works, why it boosts factual reliability, and how to use it with AI systems to generate more dependable results. Fluency vs. restraint: Which should you favor? When AI is asked to deliver full, polished responses without guidance on how to treat uncertainty or missing data, it tends to favor fluency over restraint. In other words, it keeps the answer flowing smoothly (fluency) instead of stopping, adding caveats, or refusing to answer when it lacks information (restraint). This is when AI “makes things up” – because uncertainty was never defined as a reason to stop. The fallout can be financially damaging and can erode reputation, productivity, and trust. Professional services firm Deloitte was required…