Prompt Engineering
Prompt engineering is the practice of precisely crafting the instructions given to a generative AI model, to get reliable, consistent results instead of random ones. It includes choosing the examples provided as context, structuring the expected response format, and defining the model's role and constraints.
A concrete example: a prompt that simply asks to "summarize this contract" will produce summaries that vary in length and structure from one call to the next. A prompt that specifies the output format (bullet list, fixed sections), a maximum length, and the points that must always be covered (amounts, durations, termination clauses) produces results a product can use directly. That difference in rigor shows up immediately in how reliable the feature feels to end users.
Many teams treat prompts as disposable text, tweaked by hand in production with no tracking. In practice, a prompt that works in production should be versioned and tested like code: changing a single sentence can break a use case that worked the day before. Setting up a handful of automated tests on representative cases avoids silent regressions.
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