Develop a meta-prompt that, when fed into a large language model (LLM), generates a personalized rapid learning framework for a given topic.  The framework should include: </p>
<p>1. **Learning Objectives:** Clearly defined, measurable, achievable, relevant, and time-bound (SMART) goals for the learning process.<br />
2. **Resource Identification:** A curated list of resources (books, articles, videos, courses, etc.) tailored to the specified topic and learning style (e.g., visual, auditory, kinesthetic).<br />
3. **Learning Schedule:** A structured timeline outlining daily or weekly learning activities, incorporating spaced repetition and active recall techniques.<br />
4. **Assessment Methods:** Strategies for evaluating learning progress, such as quizzes, practice exercises, projects, or knowledge application scenarios.<br />
5. **Feedback Mechanisms:** Methods for incorporating feedback and adjusting the learning plan based on progress and challenges.<br />
6. **Knowledge Consolidation Techniques:** Strategies for summarizing, synthesizing, and applying newly acquired knowledge, such as mind mapping, note-taking systems, or knowledge graph creation.</p>
<p>The meta-prompt should guide the LLM to create frameworks adaptable to various learning styles and preferences, incorporating different learning techniques such as Feynman Technique, spaced repetition, and active recall.  The output should be formatted as a structured outline, easily convertible into a document or spreadsheet.  The success of this meta-prompt will be measured by the clarity, comprehensiveness, and actionability of the generated learning frameworks.  It should produce frameworks that are easily usable by individuals with varying levels of prior knowledge on the subject. Example input: &#8220;Learn Python programming in 30 days&#8221;. Example output should include specific learning resources, a daily schedule, and assessment methods for learning Python.