Turn a long work document into a step-by-step learning path
Converts a policy, manual, playbook, or spec into short, sequenced lessons with practice and checks.
The prompt
Turn this document into a learning path I can work through step by step.
Document: [PASTE TEXT - e.g. a policy, process manual, product spec, playbook, or standard]
Who is learning: [me / a new hire / my team - and their role]
What they need to be able to do afterward: [e.g. "handle refund requests without escalating"]
Time available: [e.g. "30 minutes a day for one week"]
Steps:
1. Identify the 5-10 core ideas or procedures in the document, and the parts that are reference material (to look up, not memorize).
2. Put them in a learning order: foundations first, then procedures, then exceptions and edge cases.
3. Group them into short modules that fit the time available.
For each module return:
- Learning goal (what the learner can do after it).
- The relevant section of the document, cited by heading or page.
- A plain-language explanation (under 120 words).
- One practice task or scenario from real work.
- 3 check questions, with answers in a separate section at the end.
Also return:
1. A one-page overview of the path (modules, time, goals).
2. A quick-reference sheet of the things people look up most.
3. A final scenario that combines several modules.
4. Any parts of the document that are unclear, contradictory, or out of date, so I can confirm them with the owner.
Use only what is in the document. Do not add rules, numbers, or steps that are not there; mark gaps as [CONFIRM WITH OWNER].
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore Learning & Teaching prompts
What to fill in
- me / a new hire / my team - and their role
- e.g. "handle refund requests without escalating"
- e.g. "30 minutes a day for one week"
- CONFIRM WITH OWNER
How this prompt is built
This prompt follows the Few-Shot structure (Few-shot prompting). A few-shot prompt includes one or more worked examples of the input and the output you want, so the model can copy the pattern instead of guessing it.
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