Transparent and reviewable
How AILesson prompt recipes are designed and tested
A transparent method for choosing useful tasks, writing model-portable prompts, testing real examples, and keeping limitations visible
Start with a real outcome
Every recipe begins with a task a person needs to finish and an output they can inspect. We avoid decorative personas and instructions that do not change the result.
Collect the minimum useful context
Variables capture information that only the user knows and that can materially affect the answer. Missing information remains an explicit placeholder instead of becoming a model guess.
Make uncertainty reviewable
Templates distinguish supplied facts, assumptions, evidence, recommendations, and open questions when the task requires it. Time-sensitive claims must be checked again at the time of use.
Test both languages with real examples
A recipe is not complete until its English and Simplified Chinese versions have full, non-sensitive input and example output. The public record names the tested tools and review date.
Check the result, not the prompt’s appearance
Each recipe includes observable checks for omissions, unsupported claims, changed commitments, and unusable output. A longer prompt is not automatically a better prompt.
Keep one source of truth
A recipe has one stable page even when it appears under several topics, audiences, tools, or workflows. Tool pages only include recipes whose published test record names that tool.