Prompt engineering is the practice of giving a model clear instructions, useful context, and a precise output contract. Stronger models need less wording for simple tasks, but they still cannot infer product requirements that were never provided.
A good prompt defines the goal, identifies the input, explains important constraints, and shows what a successful answer looks like. It may also include examples or tools. This improves consistency and makes failures easier to diagnose.
Prompt engineering is only one part of an LLM system. Retrieval, tool design, schema validation, and evaluations often matter more than clever phrasing. Treat prompts as versioned application code and test them on representative cases instead of relying on one successful example.
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