Build a taxonomy of a subject or field
A clear hierarchy of fields, subfields, and topics with definitions and cross-links, for study, research, tagging, or course design.
The prompt
Build a taxonomy of a subject for me.
Subject or field: [e.g. "machine learning", "public health", "contract law"]
What I'll use it for: [e.g. "structuring a course", "tagging a content library", "mapping a literature review", "my own study plan"]
Depth needed: [e.g. "3 levels", "down to individual topics"]
Scope limits: [anything to include or exclude]
Existing scheme to align with, if any: [e.g. "ACM Computing Classification", "our current tags"]
Rules for a good taxonomy:
- Each level uses one consistent organizing principle (e.g. by method, by application, by object of study) - say which one you chose and why.
- Categories at the same level should not overlap; where real overlap exists, list it as a cross-link instead of duplicating the item.
- Use names that practitioners actually use, and note common synonyms.
- Keep sibling categories roughly comparable in breadth.
Return:
1. The hierarchy as a nested outline (Field > Subfield > Topic > Subtopic), with a one-line definition for each item at the top two levels.
2. A cross-links list: items that belong to more than one branch, and interdisciplinary connections.
3. Where the field is contested or changing fast, a short note on alternative ways it is divided.
4. The same taxonomy as a flat table (ID | Name | Parent | Level | Synonyms) I can paste into a spreadsheet.
If the subject is too broad for the depth I asked, tell me and propose where to split it.
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore Research & Summaries prompts
What to fill in
- e.g. "machine learning", "public health", "contract law"
- e.g. "3 levels", "down to individual topics"
- anything to include or exclude
- e.g. "ACM Computing Classification", "our current tags"
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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