A good example sets a pattern
When tone or format is important, include a short sample of the desired output. Samples should be representative of actual work; A few nearly identical examples do not cover the exceptions.
Prompt Engineering · From idea to clear order
Request correctly.
Evaluate accurately.
Turn the vague request into a clear order; Put together the goal, data, constraints and output shape and make it testable.
Creating the first prompt
Why try this product?
Make your wishes so clear that the model cannot misunderstand.
A good prompt is not a beautiful text; A technical specification is: role, purpose, input, limitation and output form. In this workshop, you will turn vague prompts into actionable commands step by step.
- Prompt builderSeparate the components of a standard prompt and compare the output.
- Common errorsSee why "Write a good text" doesn't work and how to fix it.
- Checklist before sendingCriteria that each prompt must meet before execution.
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"Write something good"
Not yet a brief.
Specify what needs to be done, for whom, with what information and in what format. Then measure the output with a clear standard. Giving a role to the model alone does not replace the purpose and data.
In this workshop, you will create a content brief. The output can be copied; No text input is sent to the server or AI service.
Prompt making workshop
From a vague request to a verifiable orderYour prompt is ready
Write the objective to form a draft.
Structure checking does not guarantee the quality of the model response; Test the output with some real inputs.
A change, a comparison
Try the prompt on a fixed set of inputs. Change one section in each round and compare accuracy, adherence to limits and format; Longer prompt length is not success by itself.
What to say when the data is not enough?
Write clearly so that the model does not create missing information and asks questions if needed. For example, to write an email, if the time of the event is not known, it should request it, not create an arbitrary date.
Is a JSON request enough?
The template request helps the model, but must be validated in the actual output product. Check required keys, data type and allowed values and correct path for invalid output.
A decision with you
For further reading: Prompt Engineering Guide — Anthropic
From learning to implementation