What are examples of prompt engineering?
Examples of prompt engineering include classifying a lead against an ICP, extracting product fields from a document, summarizing approved research, drafting a campaign brief, critiquing a page against editorial rules, and returning a structured object for a downstream workflow. Each prompt is designed around a specific job and testable output.
The best example depends on what is failing. A classification prompt needs clear labels and boundary cases. A research prompt needs approved sources and citation rules. A drafting prompt needs audience, purpose, evidence, and voice. A tool-using prompt needs permissions, action limits, and an escalation path.
Why prompt engineering examples matter
Concrete examples teach more than generic advice to be specific. They show where context belongs, how output requirements are expressed, and which decisions remain outside the model. They also make evaluation possible because reviewers can compare the result with a known task and expected behavior.
Store examples with the input, intended output, scoring notes, prompt version, model, and date. Separate demonstrations shown to the model from test cases used to evaluate it. When the business definition changes, revise the examples and rerun the same held-out set before replacing a production prompt.
How to use prompt engineering examples in practice
Put prompt engineering examples under the same change discipline as other business logic. The owner should know the prompt version, data sources, model, tools, expected behavior, and the consequences of an incorrect output. Keep the source beside the result and make changes reversible. This protects the operation when a definition, vendor, model, template, or buyer behavior changes after the original decision. The final review should ask what changed for a buyer or operator. If prompt engineering examples only creates another field, page, prompt, or dashboard, its role remains incomplete.
Example
A lead-research prompt receives a company URL and approved source excerpts. It must return company summary, market, employee range, recent trigger, evidence links, and an uncertainty field as JSON. The prompt forbids unsupported estimates and instructs the system to return null when the sources do not support a value. Reviewers score field accuracy and source coverage.
A useful prompt example includes clever wording and the operating boundary. It shows what the model may use, what it must produce, and what it should do when the task exceeds the evidence.