Complete the definition: prompt engineering is the practice of designing the LLM's ___ to shape its output, without modifying model ___.

Prompt engineering is the discipline of writing the text you send to an LLM so the output is what you want. The blanks are **input** and **weights**. When you call an LLM API, you control two layers: the system message and the user message. Both are just strings. Inside those strings you put instructions, output format, few-shot examples, constraints, and any context like retrieved documents or conversation history. That bundle is the prompt. Designing it well is prompt engineering. The thing you explicitly do NOT touch is the model's parameters. Those were set during training by the provider (OpenAI, Anthropic, Google, Meta) and they stay frozen at inference time. A GPT-5.5 call and the next GPT-5.5 call use the same weights; the only difference between two responses is what you sent in. That boundary is what makes prompt engineering cheap, fast, and reversible. You can change a prompt in seconds, deploy it in minutes, and roll it back if it regresses. Fine-tuning, by contrast, takes a training run, a checkpoint, and a deployment cycle.

Prompt engineering is the practice of designing the LLM's **input** to shape its output, without modifying model **weights**. The input includes instructions, output format, few-shot examples, constraints, and context (retrieved documents, conversation history, tool descriptions). All of it is text you send at inference time. The weights are the trained parameters and they stay frozen. That boundary is the whole reason the discipline exists. Prompt changes deploy in seconds with no training cost; fine-tuning changes require a training run, eval, and rollout. For most production LLM apps the right first move is to push prompt engineering as far as it goes before reaching for fine-tuning. The trade-off is ceiling. Prompts can teach the model what task to do but cannot teach it new factual knowledge or change its underlying capabilities. When you hit that ceiling, RAG (for facts) or fine-tuning (for behavior) takes over.