Learn AI by
making decisions.
Build a clearer brief. Question an impressive answer. See what happens when a system gets it wrong. Practical AI skills you can take into your next project.
12 lessons · 3 evidence cases · 4 interactive workspacesOffline learning simulations and authored examples. This lab does not call an AI model or upload your text. Field-completion checks do not predict model quality.
A better task starts with a better brief.
Your reusable prompt
Before you try it in an AI tool
- Remove information the tool is not approved to receive.
- Choose a real example with a known good answer.
- Check facts, omissions and format separately.
- Revise one instruction at a time and compare.
Labels and boundaries help organize input but do not guarantee protection against malicious instructions inside source material.
Does the source actually say that?
These fictional cases deliberately mix supported, contradicted and unstated claims. Judge against the supplied evidence, not what sounds plausible.
Source notes
A closed-source exercise: “not stated” means this packet does not establish the claim. It does not prove the claim is false in the real world.
Review the draft claims
One threshold. Different mistakes.
A fictional classifier scores support tickets for urgency. Move the threshold to decide which tickets are flagged. Scores are invented, not calibrated probabilities. Labels are provided so you can inspect errors.
The best threshold on these 12 examples can overfit this dataset. Validate on separate representative examples before using a policy in practice. Costs here are learning assumptions, not financial estimates.