Intro
Treating prompts like APIs stops drift and makes results predictable. In this post I show a compact, practical pattern you can apply today to make your prompts behave like reliable functions: explicit inputs, structured outputs, and clear error cases.
Why prompt contracts matter
When prompts are ad-hoc, outputs vary. A contract forces you to specify inputs, the expected shape of outputs, and what to do on failure. That makes testing, monitoring, and collaboration far easier.
Concrete pattern
- Inputs: list named inputs with types and examples.
- Output schema: provide a small JSON schema or bullet list of fields.
- Error cases: enumerate possible failures and how to detect them.
Example
Inputs:
- user_story: string
- acceptance_criteria: string[]
Output schema:
{
"tasks": [{"title":"string","estimate":"string"}],
"risks": ["string"]
}
Failure signals:
- Missing required fields
- Output not parseable as JSON
How to test
Automate a few unit checks: feed sample inputs, assert schema, and run a smoke test against your workflow.
Wrap-up
A one-page prompt contract will save time and prevent subtle failures. Start with one important prompt and iterate.
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