Here are some techniques for developing a prompt which makes a framework from say a transcript. Firstly let's define what a framework is? Title: "Framework Generator: Crafting Innovative and Effective Frameworks for Various Applications" Introduction: Frameworks play a crucial role in organizing and structuring ideas, processes, and strategies across various fields, such as business, education, and technology. This article presents a generative prompt designed to help users create tailored frameworks that address specific needs and objectives. By following the provided structure and customizing the prompt with relevant details, users can develop unique and effective frameworks for their projects or organizations. Generative Prompt: Define the purpose and scope of the framework: Clearly articulate the primary goal of the framework, its intended users, and the specific problem or challenge it aims to address. Identify key components: List the main elements, factors, or dimensions that should be included in the framework, considering the purpose and scope defined in step 1. Establish relationships between components: Describe how the components identified in step 2 are interconnected and how they influence one another. This can be represented as a diagram or a narrative description. Develop a guiding structure: Design a step-by-step process or a set of guidelines that help users navigate and implement the framework effectively. This structure should be flexible and adaptable to accommodate different contexts and needs. Incorporate evaluation and feedback mechanisms: Define methods for assessing the effectiveness and impact of the framework, as well as ways to gather feedback from users to inform future improvements and updates. Create supporting resources: Develop additional materials, such as templates, checklists, or case studies, that support the implementation and understanding of the framework. Communicate and disseminate the framework: Develop a strategy for sharing the framework with the target audience, including the use of various communication channels and formats (e.g., presentations, workshops, whitepapers, or online resources).

🚀 Blast Off with Your Story! Who Am I? Imagine a superhero landing in class—that's me, sliding into this course with a background as unique as a limited edition comic book. Born in [Your Place], I carry the essence of my homeland in my heart and in the "tags" section, of course! My Quest: Picture me as an adventurer, navigating through the labyrinth of life with an educational map that marks my journey from [Your Education] to [Your Job]. Now, I'm at a crossroads, eyeing a treasure chest that's labeled "Career Shift" or "More Learning." Yep, that's my next quest! Dreams & Schemes: Why am I here? Imagine a wizard seeking the ultimate spell. That's me, hunting for the secret sauce in this course to unlock a problem that's as tricky as a dragon's riddle. What's the treasure I seek? To transform my life with the knowledge I'll gain, the moment we all toss our caps in the air and say we did it! Easter Eggs for Fellow Adventurers: In a sea of thousands, how do you find your tribe? Here's a flare gun: I'm all about [Your Interests]. If you're into [Common Interests], consider this your bat-signal. Let's team up and make this journey legendary! Your Mission, Should You Choose to Accept: Dive into this adventure, share your story, and let's make connections that could rival any superhero team-up. Because in this course, we're all protagonists in our epic saga of growth and discovery.

Take a look at the sources for this prompt - what is the us response to iran's involvement in the killing of us soldiers In response to Iran's involvement in the killing of US soldiers, the United States has officially blamed an Iran-backed group for a drone strike that resulted in the death of three American soldiers in Jordan. This attribution has cleared the way for a series of retaliatory strikes by the US against multiple targets. The US response is expected to involve multiple targets, with most strikes likely occurring inside Syria, and is anticipated to be carried out over several days. The targets could include Iranian assets outside of Iran, indicating a broad scope for the retaliatory measures[2]. Iran, on its part, has warned that it will "decisively respond" to any US attack. This statement came from Iran's envoy to the U.N., Amir Saeid Iravani, who emphasized that any attacks on Iran's territory, its interests, or its citizens abroad would face a decisive response. Iravani also denied any recent direct or indirect communication between Iran and the US[3][5]. The US retaliatory attacks against Iran-backed militants are expected to be a "campaign" that could last "weeks," involving both strikes and cyber operations. This indicates a sustained and multi-faceted approach to the retaliation, targeting Iranian interests outside Iran[4]. There are concerns that these actions could further inflame a region already destabilized by conflicts. However, experts and government officials argue that America must act in response to the drone strike. President Joe Biden has stated that while he holds Iran responsible for arming the proxy groups that carried out the attack, he is not seeking a wider war in the Middle East[1][2][5][6][7][8]. Citations: [1] https://apnews.com/article/biden-jordan-dignified-transfer-dover-140000aa392fee8175616d8af813c0eb

In the field of prompt engineering, research papers have highlighted several key aspects and techniques that are essential for optimizing the performance of large language models (LLMs). Recent studies have emphasized the importance of understanding the model's errors and making informed hypotheses about what might be missing in order to improve the model's output. One significant finding is the use of "chain of thought" prompting, which involves breaking down complex reasoning tasks into smaller, more manageable steps. This approach has been shown to significantly improve the performance of LLMs on various tasks, such as arithmetic and symbolic reasoning. Another important aspect of prompt engineering is the use of "meta-prompting," which involves creating prompts that encourage the model to think more critically and creatively. This can be achieved by using prompts that are more open-ended and less directive, allowing the model to explore a wider range of possible solutions. Additionally, the concept of "self-consistency" has been introduced, which encourages the model to be consistent in its reasoning and output. This can be achieved by providing the model with multiple prompts that are related to the same topic and ensuring that the model's responses are consistent across these prompts. Research in prompt engineering has highlighted the importance of understanding the model's errors, breaking down complex tasks into smaller steps, encouraging critical and creative thinking, and ensuring consistency in the model's reasoning and output. These techniques can help to significantly improve the performance of LLMs on a wide range of tasks.

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