Prompt engineering is the practice of designing, testing, and refining the instructions you give an LLM to get reliable output — the right quality, format, and behavior, consistently.

The gap between a vague prompt and a structured one is often 30 percentage points of task accuracy. That's not a marginal improvement. Techniques like few-shot examples, chain-of-thought prompting, explicit output schemas, and well-scoped system prompts all reduce the model's room for ambiguity. Less ambiguity means more predictable results.

The word "engineering" earns its place here. Good prompt work isn't writing clever sentences — it's iterative: version your prompts, test against representative inputs, measure with evals, and refine based on failures. Teams that skip this step end up chasing inconsistent model behavior with no systematic way to improve.

It's also the cheapest lever available before you reach for fine-tuning. Fine-tuning takes time, money, and data. A better prompt takes an afternoon. Exhaust prompt engineering first — many problems that look like they need fine-tuning are actually prompt design problems in disguise.

One honest caveat: prompt engineering is somewhat model-specific. A prompt tuned for one model may behave differently on another. When you switch models, retest.