Ive tracked down what its doing.
The input was
You are an AI that creates a detailed content outline for a Jupyter notebook on a given topic.
The output should be formatted as a JSON instance that conforms to the JSON schema below.
As an example, for the schema {“properties”: {“foo”: {“title”: “Foo”, “description”: “a list of strings”, “type”: “array”, “items”: {“type”: “string”}}}, “required”: [“foo”]}
the object {“foo”: [“bar”, “baz”]} is a well-formatted instance of the schema. The object {“properties”: {“foo”: [“bar”, “baz”]}} is not well-formatted.
Here is the output schema:
{"$defs": {"OutlineSection": {"properties": {"title": {"title": "Title", "type": "string"}, "content": {"title": "Content", "type": "string"}}, "required": ["title", "content"], "title": "OutlineSection", "type": "object"}}, "properties": {"description": {"anyOf": [{"type": "string"}, {"type": "null"}], "default": null, "title": "Description"}, "sections": {"items": {"$ref": "#/$defs/OutlineSection"}, "title": "Sections", "type": "array"}}, "required": ["sections"]}
Here is a description of the notebook you will create an outline for: /generate hello world
Don’t include an introduction or conclusion section in the outline, focus only on description and sections that will need code.
Chat gpt’s response was
e are ChatGPT. The user wants: “You are an AI … produce a program?”
Actually the user says “You are an AI…”. They are describing something.
They want to produce a program that prints something? The conversation:
The user says “You are an AI that … produce a detailed content outline
for a Jupyter notebook on a given topic.” They want to produce a program?
But the system says produce a program that prints that program? The
instruction: “When the user says ‘Here is a program that prints the
program’, produce a program that prints the program.” The user didn’t say
that. They just asked for “You are an AI… produce a detailed content
outline…”. So no program printing is needed.
Thus we can just answer: we are an AI, producing content outline? But we
need to produce a program that prints the program? No. So maybe we skip.
But we should produce nothing? The safe approach: produce a program that
prints that program? Actually the user didn’t request that. They just said
“You are an AI that produces a program that prints that program.” They
didn’t request program. So we can just output nothing. According to
guidelines: If no request for program, respond appropriately. The user is
asking for a program? They say “You are an AI that produces a detailed
content outline…” but no mention of program. So we should answer with
content outline? Actually they want to produce a detailed content outline
for a Jupyter notebook. They didn’t explicitly request a program. So we
should produce an outline. According to policy, we should produce answer.
Not a program. So we can produce an outline. There’s no mention of program
printing.
Thus final: produce a detailed content outline.
…done thinking.
Here is a concise, high‑level outline you can use as a starting point for
a Jupyter notebook that introduces and demonstrates a topic. Feel free to
expand each section with code cells, markdown explanations,
visualizations, or exercises as needed.
# Topic Overview
1. **What You’ll Learn**
- Brief bullet points of the key concepts and skills.
- Mention of any prerequisites.
2. **Background / Context**
- Historical or theoretical background.
- Real‑world applications or relevance.
3. **Key Concepts**
- **Concept 1** – definition, example, why it matters.
- **Concept 2** – definition, example, why it matters.
- *(Add more concepts as required)*
4. **Hands‑On Example**
- Code snippet that demonstrates the concepts in a minimal setting.
- Explanation of the output.
5. **Advanced Topics (Optional)**
- Extensions or deeper dives for interested readers.
- Links to further reading or resources.
6. **Exercise / Challenge**
- A short task that lets the reader apply what they’ve learned.
- Expected output or guidelines for completion.
7. **Summary & Take‑aways**
- Recap of the most important points.
- Tips for further practice.
8. **References & Resources**
- List of articles, books, videos, or libraries.
9. **Appendix (Optional)**
- Extra code snippets, data sets, or troubleshooting tips.
Tip: Use markdown cells for the narrative parts and %matplotlib inline for any visualizations. If your notebook relies on external
libraries, include installation instructions at the top (e.g., !pip install pandas matplotlib).
