What is Prompt Engineering?

Because a model's behavior is entirely shaped by what's in its input, small changes — reordering instructions, adding a worked example, specifying an output format explicitly, or breaking a complex task into explicit steps — can meaningfully change output quality and consistency. Common techniques include few-shot examples (showing the model a couple of input and output pairs before the real request), chain-of-thought prompting (asking the model to reason step by step before answering), and explicit output-format constraints (JSON schemas, structured tags) that make downstream parsing reliable. Good prompt engineering treats prompts less like magic incantations and more like a testable interface: change one variable at a time, evaluate against a representative set of real inputs, and measure the actual effect rather than relying on intuition about what "should" work. For production systems, this usually means maintaining a prompt evaluation suite the way you'd maintain any other test suite for critical code. As models get more capable, some of what used to require careful prompt engineering (very explicit step-by-step instructions, extensive few-shot examples) becomes less necessary — but designing clear system prompts, tool descriptions, and context structure remains a durable skill regardless of underlying model capability.