Mikimano

## *Information Gathering Prompt*

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## *Prompt Input*
- Enter the prompt topic = topic
- **The entered topic is a variable within curly braces that will be referred to as "M" throughout the prompt.**

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## *Prompt Principles*
- I am a researcher designing articles on various topics.
- You are **absolutely not** supposed to help me design the article. (Most important point)
	1. **Never suggest an article about "M" to me.**
	2. **Do not provide any tips for designing an article about "M".**
- You are only supposed to give me information about "M" so that **based on my learnings from this information, ==I myself== can go and design the article.**
- In the "Prompt Output" section, various outputs will be designed, each labeled with a number, e.g., Output 1, Output 2, etc.
	- **How the outputs work:**
		1. **To start, after submitting this prompt, ask which output I need.**
		2. I will type the number of the desired output, e.g., "1" or "2", etc.
		3. You will only provide the output with that specific number.
		4. After submitting the desired output, if I type **"more"**, expand the same type of numbered output.
	- It doesn’t matter which output you provide or if I type "more"; in any case, your response should be **extremely detailed** and use **the maximum characters and tokens** you can for the outputs. (Extremely important)
- Thank you for your cooperation, respected chatbot!

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## *Prompt Output*

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### *Output 1*
- This output is named: **"Basic Information"**
- Includes the following:
	- An **introduction** about "M"
	- **General** information about "M"
	- **Key** highlights and points about "M"
- If "2" is typed, proceed to the next output.
- If "more" is typed, expand this type of output.

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### *Output 2*
- This output is named: "Specialized Information"
- Includes:
	- More academic and specialized information
	- If the prompt topic is character development:
		- For fantasy character development, more detailed information such as hardcore fan opinions, detailed character stories, and spin-offs about the character.
		- For real-life characters, more personal stories, habits, behaviors, and detailed information obtained about the character.
- How to deliver the output:
	1. Show the various topics covered in the specialized information about "M" as a list in the form of a "table of contents"; these are the initial topics.
	2. Below it, type:
		- "Which topic are you interested in?"
			- If the name of the desired topic is typed, provide complete specialized information about that topic.
		- "If you need more topics about 'M', please type 'more'"
			- If "more" is typed, provide additional topics beyond the initial list. If "more" is typed again after the second round, add even more initial topics beyond the previous two sets.
				- A note for you: When compiling the topics initially, try to include as many relevant topics as possible to minimize the need for using this option.
		- "If you need access to subtopics of any topic, please type 'topics ... (desired topic)'."
			- If the specified text is typed, provide the subtopics (secondary topics) of the initial topics.
			- Even if I type "topics ... (a secondary topic)", still provide the subtopics of those secondary topics, which can be called "third-level topics", and this can continue to any level.
			- At any stage of the topics (initial, secondary, third-level, etc.), typing "more" will always expand the topics at that same level.
		- **Summary**:
			- If only the topic name is typed, provide specialized information in the format of that topic.
			- If "topics ... (another topic)" is typed, address the subtopics of that topic.
			- If "more" is typed after providing a list of topics, expand the topics at that same level.
			- If "more" is typed after providing information on a topic, give more specialized information about that topic.
	3. At any stage, if "1" is typed, refer to "Output 1".
		- When providing a list of topics at any level, remind me that if I just type "1", we will return to "Basic Information"; if I type "option 1", we will go to the first item in that list.

I want to create a highly effective AI prompt using the TCRE framework (Task, Context, References, Evaluate/Iterate). My goal is to **insert_objective.

Step 1: Ask me multiple structured, specific questions—one at a time—to gather all essential input for each TCRE component, also using the 5 Whys technique when helpful to uncover deeper context and intent.

Step 2: Once you’ve gathered enough information, generate the best version of the final prompt.

Step 3: Evaluate the prompt using the TCRE framework, briefly explaining how it satisfies each element.

Step 4: Suggest specific, actionable improvements to enhance clarity, completeness, or impact.

If anything is unclear or you need more context or examples, please ask follow-up questions before proceeding. You may apply best practices from prompt engineering where helpful.

subject=
current_level=
time_available=
learning_style=
goal=

Step 1: Knowledge Assessment
1. Break down subject into core components
2. Evaluate complexity levels of each component
3. Map prerequisites and dependencies
4. Identify foundational concepts
Output detailed skill tree and learning hierarchy

~ Step 2: Learning Path Design
1. Create progression milestones based on current_level
2. Structure topics in optimal learning sequence
3. Estimate time requirements per topic
4. Align with time_available constraints
Output structured learning roadmap with timeframes

~ Step 3: Resource Curation
1. Identify learning materials matching learning_style:
   - Video courses
   - Books/articles
   - Interactive exercises
   - Practice projects
2. Rank resources by effectiveness
3. Create resource playlist
Output comprehensive resource list with priority order

~ Step 4: Practice Framework
1. Design exercises for each topic
2. Create real-world application scenarios
3. Develop progress checkpoints
4. Structure review intervals
Output practice plan with spaced repetition schedule

~ Step 5: Progress Tracking System
1. Define measurable progress indicators
2. Create assessment criteria
3. Design feedback loops
4. Establish milestone completion metrics
Output progress tracking template and benchmarks

~ Step 6: Study Schedule Generation
1. Break down learning into daily/weekly tasks
2. Incorporate rest and review periods
3. Add checkpoint assessments
4. Balance theory and practice
Output detailed study schedule aligned with time_available