Learn AI Workflows
AI outputs are proposals until validation and appropriate review make them usable.
Read each short explanation, work through the example, then answer a pop quiz. Every answer explains the reasoning. Incorrect answers unlock a fresh question on the same idea; the full lesson contains ten questions.
01Understand the basics
Ready to learnAn AI workflow puts a model inside explicit steps. The model can draft or classify, while ordinary code checks structure, applies rules and controls actions. Decide how uncertainty and failure are handled before connecting the output to an external action.
02Use complementary checks
Ready to learnA schema checks the output shape; evidence checks support factual claims; business rules check allowed values and actions. These checks answer different questions.
Try a different value
03Define the fallback
Ready to learnReview and fallback paths make uncertain cases operationally manageable. Give reviewers the evidence and proposed action, and ensure the workflow can stop before an irreversible effect.
You’ve practised all three sections.
First-attempt understanding and later practice are shown separately. Keep practising with the related tools or continue through the suggested path.
Put it to work
Apply what you’ve learned using the related tools.