Understand how AI generates output, where it can fail, and how to check the result. Compare approaches to a bounded task and assess quality, time and costs before deciding how to use the work.
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The Tool asked you to choose the smallest useful leverage. A checklist, conversation or non-AI method may still be the right answer. This stage teaches AI literacy: how to test AI's contribution, recognise its limits and keep responsibility for the result. Learning that method does not require adopting AI for your chosen solution.
The checkpoint includes an AI-assisted comparison as a learning exercise. Use your Tool trial if it is suitable. If it is not, use a separate low-risk practice task with permitted, non-sensitive inputs. Keep the original solution unless the comparison gives you a reason to change it. Compare the whole task, including checking, corrections, time and tool costs—not just generation speed. Finding that AI adds no useful value is a valid conclusion from the exercise.
Practice brief — fictional, not customer work
If you need a supplied task, draft two product descriptions, each 60–80 words, for a fictional stationery shop. Exercise-only facts: its notebook is A5, undated, with 80 pages and a recycled-card cover; its pen cup is steel, 10 cm tall and holds desk pens. These details are invented for practice, not verified information about real products. No price, delivery promise, certification, waterproofing claim or other specification is supplied.
The checking standard is: both descriptions meet their word limits; each contains the supplied facts for its product; neither adds unsupported facts, prices or promises. Count the words yourself or with a separate counter, and check each factual statement against this brief. Do not use the model's own claim of correctness as the check.
Carry the same task through the lessons: decide what context it needs, which transformation you are testing, which claims need checking and what you will measure. At the checkpoint, draft it without AI first, then make an AI-assisted version from the same brief. Keep both drafts and your independent checks privately. Compare total time, corrections, actual tool costs and final compliance with the brief. Record any remaining uncertainty. Familiarity with the brief or the first draft can affect the second version; this one ordered comparison does not establish a dependable improvement or isolate AI's contribution. These are practice outputs, not client work, approved advertising or a paid result.
An LLM was trained by reading billions of documents and compressing their patterns into billions of numbers (weights). When you prompt it, it predicts the best next word, then the next, until a full answer exists.
"Predicting words" sounds shallow, but to predict well across law, code, marketing, and medicine, the model had to internalize the structures of those fields. That's why it can draft a contract clause and debug Python in the same conversation.
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Stage 1 opens first; completing its lessons opens Stage 2, and so on. Work privately and publish only optional, non-sensitive summaries. Lesson completion is not a verified outcome.
Lesson counts do not measure workload. Minute figures are supplied durations or reading estimates, not measured completion time. Practice and review may take longer; observation and response periods add elapsed calendar time.