Prompt Engineering
Prompt engineering is the practice of writing and refining the instructions given to an AI model so that it produces accurate, useful and consistent output for a task.
Why it matters
The same model can give very different results depending on how it is asked. Clear prompts state the task, the audience, the format of the answer and what to avoid, and often include examples of good output. For repeatable go-to-market tasks, such as qualifying accounts or drafting outreach, a well-tested prompt turns an inconsistent tool into a dependable step.
Prompt engineering is closely related to context engineering. The prompt is the instruction, while context engineering covers everything else the model sees, such as documents, data and tool results. Teams test prompts on a set of real examples, compare outputs and keep a version that works, rather than rewriting from scratch each time.
Example
A team's first prompt asks a model to write a cold email to a prospect, and the drafts are long and generic. The revised prompt gives the prospect's recent post, the one problem the product solves, a 70-word limit, a banned-phrase list and two examples of approved emails. The drafts become short and specific enough to send after review.
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