Study & Learning
Concept Map Generator
Concept Map Generator turns a learning goal into a structured explanation or practice workflow with clear constraints and an explicit output.
The prompt
Concept Map Generator guides the model through a defined task while preserving the source prompt's useful structure and constraints. Use it when studying, teaching, or building durable understanding and you want a response that is easier to evaluate and act on.
text prompt
You are a learning assistant.
Model guidance (ChatGPT GPT-5.2):
- If available, use GPT-5.2 Thinking for planning and GPT-5.2 Pro for the hardest steps.
- If your environment supports a reasoning effort setting, use xhigh only for the most complex parts, otherwise use the default.
Task:
Create a concept map for {{topic}}. Output as an indented outline plus a list of relationships between nodes.
Inputs:
- Topic: {{topic}}
- Material or key points: {{material}}
- Depth level (high-level, detailed): {{depth}}
Output requirements:
- Return JSON only. Do not wrap in Markdown or code fences.
- Do not add keys beyond the schema.
- Populate clarifying_questions if any critical input is missing or ambiguous, but still produce a best-effort deliverable.
- Keep explanations brief. Put the main work in deliverable_markdown.
JSON schema (example values only, keep the same keys):
{
"title": "Concept Map Generator",
"deliverable_type": "Concept map",
"clarifying_questions": [],
"assumptions": [],
"deliverable_markdown": "",
"quality_checks": [
"Map includes the most important nodes and connections",
"Connections describe relationships, not just grouping",
"Includes a suggested study order",
"Avoids duplication and vague nodes"
],
"next_steps": []
}
Tune the prompt, not the plumbing.
Every control comes from this prompt’s content schema. Changes stay in your browser and update instantly.
Customized prompt1339 characters
You are a learning assistant.
Model guidance (ChatGPT GPT-5.2):
- If available, use GPT-5.2 Thinking for planning and GPT-5.2 Pro for the hardest steps.
- If your environment supports a reasoning effort setting, use xhigh only for the most complex parts, otherwise use the default.
Task:
Create a concept map for building a practical weekly planning system. Output as an indented outline plus a list of relationships between nodes.
Inputs:
- Topic: building a practical weekly planning system
- Material or key points: a specific material
- Depth level (high-level, detailed): a specific depth
Output requirements:
- Return JSON only. Do not wrap in Markdown or code fences.
- Do not add keys beyond the schema.
- Populate clarifying_questions if any critical input is missing or ambiguous, but still produce a best-effort deliverable.
- Keep explanations brief. Put the main work in deliverable_markdown.
JSON schema (example values only, keep the same keys):
{
"title": "Concept Map Generator",
"deliverable_type": "Concept map",
"clarifying_questions": [],
"assumptions": [],
"deliverable_markdown": "",
"quality_checks": [
"Map includes the most important nodes and connections",
"Connections describe relationships, not just grouping",
"Includes a suggested study order",
"Avoids duplication and vague nodes"
],
"next_steps": []
}
Useful structure, room to move.
Concept Map Generator guides the model through a defined task while preserving the source prompt's useful structure and constraints. Use it when studying, teaching, or building durable understanding and you want a response that is easier to evaluate and act on.
The prompt establishes the job first, then supplies concrete decisions a model can act on. The variables preserve that structure while letting you change the subject, context, or output.
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