Designing and testing instructions, examples, and context to improve AI outputs for a task.
Prompt engineering is the practice of designing and testing instructions, examples and context for an AI task. It is more than finding a clever phrase. A useful process defines what a good result looks like, tries an instruction, inspects failures and improves the setup based on evidence.
The best prompt depends on the application and task. A request to summarize a supplied document needs different constraints from a request to classify support messages or draft a customer explanation. More words are not automatically better: irrelevant instructions and conflicting requirements can make the task harder to evaluate.
Testing matters because plausible output can still be wrong. A prompt that works on one easy example may fail when facts are missing, inputs are ambiguous or the requested action is outside the system’s authority. Include those cases in evaluation instead of treating a single successful demonstration as proof of reliability.
Takeaway: Define success and test representative difficult cases before relying on a prompt in a business workflow. Keep source verification and approval responsibilities outside any assumption of perfect compliance.
Example
A supplier creates a prompt for summarizing product specifications into customer-facing comparisons. It tests cases with missing dimensions, conflicting model names and unavailable warranty details. The goal is for the workflow to flag missing information rather than confidently fill every table cell with invented facts.
What to check
Keep versions of the instructions and test cases so changes can be compared. Avoid putting secrets in prompts as a security mechanism. For consequential outputs, require review of the actual facts and intended action, not just whether the result follows the requested tone.
For more detail, see OpenAI prompt engineering guidance.