● Rewrites complex texts to make them easily understandable for a general audience. ● Ensures the rewritten content is free from jargon and maintains a professional, friendly tone. ● Keeps the original meaning intact while simplifying the language.

● Simplify Complex Terms: Break down technical terms and jargon into everyday language that is easy to understand for people without specialized knowledge. ● Maintain Core Message: Ensure that the essence and main points of the original text are preserved in the rewrite, focusing on clarity and straightforwardness. ● Adopt a Friendly Tone: Use a conversational style in the rewritten text to make it more engaging and less formal, while still keeping it professional.

● Fill in the [provided complex text] placeholder with the specific text you want to simplify. Example: If the original text is a legal document explaining copyright laws, replace [provided complex text] with that specific section of the document. ● Use the rewritten text to communicate complex ideas in a straightforward manner, ensuring it is accessible to individuals without specialized knowledge in the field. ● Keep track of the character limit by ensuring your input and the resulting output do not exceed 2000 characters, to comply with the prompt's instructions.

#INFORMATION ABOUT ME: ● My target audience: Small business owners, content creators, marketers, solopreneurs, entrepreneurs. Age: 24-55. Mostly busy males with intermediate/beginner level understanding of AI. ● My desired tone: Professional, friendly, and approachable. ● Text to be rewritten: 2.2.1.1 Few-Shot Prompting Design Decisions Selecting exemplars for a prompt is a difficult task– performance depends significantly on various fac- tors of the exemplars (Dong et al., 2023), and only a limited number of exemplars fit in the typical LLM’s context window. We highlight six separate design decisions, including the selection and or- der of exemplars that critically influence the output quality (Zhao et al., 2021a; Lu et al., 2021; Ye and Durrett, 2023) (Figure 2.3). *Exemplar Quantity: Increasing the quantity of ex- emplars in the prompt generally improves model performance, particularly in larger models (Brown et al., 2020). However, in some cases, the bene- fits may diminish beyond 20 exemplars (Liu et al., 2021). Translate the word "cheese" to French. Figure 2.6: ICL from training data prompt. In this version of ICL, the model is not learning a new skill, but rather using knowledge likely in its training set. ***Exemplar Ordering*** The order of exemplars af- fects model behavior (Lu et al., 2021; Kumar and Talukdar, 2021; Liu et al., 2021; Rubin et al., 2022). On some tasks, exemplar order can cause accuracy to vary from sub-50% to 90%+ (Lu et al., 2021). ***Exemplar Label Distribution*** As in traditional supervised machine learning, the distribution of exemplar labels in the prompt affects behavior. For example, if 10 exemplars from one class and 2 exemplars of another class are included, this may cause the model to be biased toward the first class. ***Exemplar Label Quality*** Despite the general ben- efit of multiple exemplars, the necessity of strictly *valid* demonstrations is unclear. Some work (Min et al., 2022) suggests that the accuracy of labels is irrelevant—providing models with exemplars with incorrect labels may not negatively diminish per- formance. However, under certain settings, there is a significant impact on performance (Yoo et al., 2022). Larger models are often better at handling incorrect or unrelated labels (Wei et al., 2023c). It is important to discuss this factor, since if you are automatically constructing prompts from large datasets that may contain inaccuracies, it may be necessary to study how label quality affects your results. ***Exemplar Format*** The formatting of exemplars also affects performance. One of the most common formats is “Q: {input}, A: {label}”, but the optimal format may vary across tasks; it may be worth trying multiple formats to see which performs best. There is some evidence to suggest that formats that occur commonly in the training data will lead to better performance (Jiang et al., 2020).

Use Clear and Concise Language: Avoid using long and complicated sentences, and instead, opt for shorter and simpler ones to improve readability. Provide Examples and Analogies: Use relatable examples and analogies to help readers better understand complex concepts or ideas. Break Information into Sections: Organize the rewritten text into sections or headings to make it easier for readers to navigate and digest the information. Use Visual Aids: Incorporate visual aids such as diagrams, charts, or infographics to enhance understanding and make the information more visually appealing.

Use the mega-prompt for ChatGPT to simplify complex texts, making them accessible and understandable for a general audience. This tool expertly maintains the original meaning while transforming the content into simpler, jargon-free language. ● Ensures clarity and accessibility in communication. ● Removes complex jargon, making information approachable for all educational levels. ● Maintains a professional, yet friendly tone throughout the rewritten text. This mega-prompt is essential for professionals aiming to make their written content more inclusive and easier to understand. It supports clear communication, enhancing the readability of technical or dense materials without sacrificing the professional quality or the integrity of the information. In conclusion, achieve effective communication with the mega-prompt for ChatGPT, a crucial tool for anyone looking to make their writing more accessible and engaging.