Prompt Engineering
Writing the instructions and context you send to an LLM so it produces the output you actually want. Real engineering, not a magic incantation.
Prompt engineering is the practice of structuring what you send to a model: the task description, the examples, the constraints, the output format, and the context. The model has no idea what you want until you tell it, and how you tell it changes the result dramatically. A vague prompt gets a vague answer. A precise prompt with examples and a defined output schema gets something you can actually ship.
The hype frames this as a mystical skill. The reality is more mundane and more useful: it is iterative engineering. You write a prompt, you test it against real cases, you see where it fails, you tighten the instructions or add examples, you measure again. The system prompt (the standing instructions that sit above every user message) is where most of the durable behavior lives, so that is where the real work goes.
Here is the honest part: prompt engineering is real, but it is not a career moat. The techniques are learnable in a week, and the models keep getting better at understanding sloppy prompts. What does not commoditize is knowing which problem to point the model at, wiring it into a real system, and evaluating whether the output is good enough to trust. That is engineering, and it is what we do under Artificial Intelligence.
Watch for anyone selling “prompt engineering” as a standalone product. The prompt is the cheap part. The expensive part is everything around it: retrieval, evaluation, guardrails, and the integration that turns a clever prompt into a reliable feature.