An AI use case should connect a task with available data, a baseline and a way to evaluate outcomes. Human review may remain necessary when errors have meaningful consequences.
Before you begin
Know Python, tabular data handling and basic statistics. Use fictional or permitted data and identify the prediction or evaluation task.
Work through the idea
Keep an experiment record with the dataset, split, baseline, metric and limitations. Choose information that would genuinely be available at the time of prediction. Compare the method with a simple alternative and inspect mistakes. A learning experiment should not be presented as a validated production decision system.
- Define the user and task
- Identify allowed data
- Choose an evaluation and fallback
Practice task
Assess a fictional support-ticket routing use case before implementing it.
Check your result
Document success criteria, common errors and how a user corrects a wrong routing decision.
A mistake to avoid
Do not add AI only because it is available or market a demonstration as a validated system.
Keep a review note
Save the inputs or scenario, your reasoning and the result you checked. Explain one assumption and one case that needs more work. Revise the exercise after feedback and distinguish what you demonstrated from what remains untested.
Official reference for further study
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