Prompt engineering is the practice of crafting your inputs to get better outputs from AI models. It's not programming in the traditional sense, but it does require skill and systematic thinking. Simple techniques include being specific about what you want, providing examples of good outputs (few-shot prompting), assigning the model a role ("You are an expert tax adviser") and structuring complex requests into clear steps. More advanced techniques involve breaking tasks into smaller subtasks, asking the model to think through problems step by step, or providing a template for the response format. What makes prompt engineering both powerful and frustrating is that small changes in wording can produce dramatically different results, and what works for one model might not work for another. There's a growing body of knowledge about what works, but it's still partly art, partly science. For businesses, prompt engineering is often the highest-leverage AI investment: before spending money on fine-tuning or custom models, optimising your prompts can deliver substantial improvements at zero additional cost. The main risk is fragility - prompts that work perfectly today might produce different results when the model is updated, so treating prompts as code that needs version control and testing is wise.