How to Simplify Prompt Engineering Concepts

I'm not gonna bait you with another simple hook about using AI wrong. But there is a super simple framework that makes me unreal better at using AI that I'd like to share. This is the most simple Prompt you should use everytime. 𝗧𝗵𝗲 𝗥𝗜𝗦𝗘𝗡 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸: 𝗥: 𝗥𝗼𝗹𝗲 - Define the AI's persona. Are you speaking to a seasoned copywriter, a financial analyst, or a marketing guru? Setting the role tunes the AI's tone and focus. 𝗜: 𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻𝘀 - Be clear and precise. "Write a blog article about the impact of AI on modern business practices." 𝗦: 𝗦𝘁𝗲𝗽𝘀 - Guide the AI through the process. "Start with an engaging intro, include real-world examples, and wrap up with actionable tips." 𝗘: 𝗘𝗻𝗱 𝗚𝗼𝗮𝗹 - Clarify the purpose. "This article should empower entrepreneurs and business leaders to integrate AI into their strategies effectively." 𝗡: 𝗡𝗮𝗿𝗿𝗼𝘄𝗶𝗻𝗴 - Set boundaries to focus the AI's creativity. "Keep the blog between 300-500 words, make it insightful yet easy to digest, and use a conversational tone." By employing the RISEN framework, you elevate a simple prompt into a powerful directive. Here's what a RISEN-inspired prompt looks like:  "As a seasoned copywriter, draft a blog article about the transformative role of AI in modern business. Start with a catchy introduction, illustrate with current examples, and conclude with tips for business integration. Aim this piece at forward-thinking entrepreneurs, ensuring it's between 300-500 words, is engaging, and uses a tone that resonates with humans, not robots." The difference is night and day. This approach to AI interaction will honestly change the game for you. #BusinessCommunication #ArtificialIntelligence #ChatGPT #PromptEngineering #Innovation

Prompt Engineering: Strategies and tactics for getting better results from large language models from the team at OpenAI. "6 prompt strategies for getting better results. 1. Write clear instructions These models can’t read your mind. If outputs are too long, ask for brief replies. If outputs are too simple, ask for expert-level writing. If you dislike the format, demonstrate the format you’d like to see. The less the model has to guess at what you want, the more likely you’ll get it. 2. Provide reference text Language models can confidently invent fake answers, especially when asked about esoteric topics or for citations and URLs. In the same way that a sheet of notes can help a student do better on a test, providing reference text to these models can help in answering with fewer fabrications 3. Split complex tasks into simpler subtasks Just as it is good practice in software engineering to decompose a complex system into a set of modular components, the same is true of tasks submitted to a language model. Complex tasks tend to have higher error rates than simpler tasks. Furthermore, complex tasks can often be re-defined as a workflow of simpler tasks in which the outputs of earlier tasks are used to construct the inputs to later tasks. 4. Give the model time to "think" If asked to multiply 17 by 28, you might not know it instantly, but can still work it out with time. Similarly, models make more reasoning errors when trying to answer right away, rather than taking time to work out an answer. Asking for a "chain of thought" before an answer can help the model reason its way toward correct answers more reliably. 5. Use external tools Compensate for the weaknesses of the model by feeding it the outputs of other tools. For example, a text retrieval system (sometimes called RAG or retrieval augmented generation) can tell the model about relevant documents. A code execution engine like OpenAI's Code Interpreter can help the model do math and run code. If a task can be done more reliably or efficiently by a tool rather than by a language model, offload it to get the best of both 6. Test changes systematically Improving performance is easier if you can measure it. In some cases a modification to a prompt will achieve better performance on a few isolated examples but lead to worse overall performance on a more representative set of examples. Therefore to be sure that a change is net positive to performance it may be necessary to define a comprehensive test suite (also known as an "eval")." Check out the detailed tactics linked to each strategy in the link below: #ai #genai #promptengineering https://lnkd.in/efXdG4TK

Yesterday I had the pleasure of working with leaders and teachers from L’Anse Creuse School District outside of Detroit for one of our Train-the-Trainer Institutes. We had a great time digging into all things GenAI!   Our 1-day institute focuses on two key PD sessions: Introduction to Generative AI for Educators and Prompting 101. We work to upskill the new trainers on foundational concepts of GenAI, before equipping them with strategies to turnkey this work in their school. In our Prompting 101 session we focus on strategies for getting the best out of popular and powerful free GenAI tools like ChatGPT, Claude, and Gemini.   What's great is there are many different prompt frameworks out there for educators to use - including our 5S Framework: Set the scene (priming), be Specific, Simplify language, Structure output, and Share feedback. We also break down a good prompting in the following four steps: 1.      Clarity is Key   Explicitly state what you would like the model to do. The more specific your prompt, the more accurate and tailored the AI's response will be. General prompts will result in general responses. 2. Pick the Right Prompting Technique You may be able to get what you need from one well-structured prompt (one-shot prompting), but there are other techniques too. You can provide examples in your prompt to guide the AI's responses (few-shot prompting), or cut down your requests into steps (chain-of-thought prompting). 3.      Provide Context   The chatbot is called a "context window" for a reason! Give AI as much necessary background information as possible. This will help it prepare a response that fits your needs.   4.      Format Matters   A well-structured prompt guides the AI in understanding the exact nature of your request. Use clear and direct language, and structure your prompt logically.   So what does that look like in practice for a one-shot prompt?   An OK prompt for educators might look like this:   “Create a lesson plan about multiplying fractions for 5th graders”   A better prompt would look like:   “Act as an expert mathematician and a teacher skilled in designing engaging learning experiences for upper elementary students. Design a lesson plan about multiplying fractions for 5th grade students.”   And an even more effective prompt would be:   “You are an expert mathematician and teacher skilled in Universal Design for Learning. Design an accessible lesson plan about multiplying fractions for 5th grade students interested in soccer. The lesson should include a hands-on activity and frequent opportunities for collaboration. Format your response in a table.”   We take this approach every time we create on of our more than 100 customizable prompts in our Prompt Library. You can check out or complete prompt library here: https://lnkd.in/evExAZSt. AI for Education #teachingwithAI #promptengineering #GenAI #aieducation #aiforeducation