Prompt engineering, explained
A prompt can include more than a question. It may also contain context, examples, limits, and a description of the result you need.
Prompt engineering is the practice of designing, testing, and refining instructions so a generative AI model produces a result that better fits the intended job.
A good prompt makes the job clear
Start with the outcome. Name the audience, the information the model should use, and any rule the response must follow. Include an example when the expected shape would otherwise be hard to explain.
Specific instructions make review easier. You can compare the response with the stated goal instead of judging whether it merely sounds convincing.
Refinement follows the result
Prompt engineering is an iterative process. You try the prompt, inspect what the model returned, and change the instruction where the result missed the need.
Do not hide a missing business rule behind more elaborate wording. Add the actual rule, source, or decision criterion the model lacked.
Prompt engineering terms
- Prompt
- The instructions, question, and context given to an AI model.
- Example
- A sample input or output that shows the model the expected shape of the result.
- Iteration
- A new attempt made after reviewing a response and changing the instructions.
What to put in a useful prompt
A clear goal
State what the result should help you decide, create, or complete.
Relevant context
Include the facts and source material the model needs for this request.
A result format
Say how the answer should be organized so you can use and review it.
Questions about prompt engineering
No. Clear knowledge of the job and its rules often matters more than technical vocabulary.
An example shows the expected format or level of detail. It can make an otherwise broad instruction more concrete.
No. It can guide the response, but the model can still make mistakes. Verify important facts and test results that affect real work.
Related glossary terms
Start with the outcome you need
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