Rick Abbott’s Post

Excited to share my latest video: "Tree of Thoughts (TOT) Prompt Engineering: Advanced Prompting Techniques!!" In this video, Explore the powerful Tree of Thoughts (TOT) method and its advanced techniques for better AI prompts. Watch here: https://lnkd.in/gm358Mm2 Don't forget to like, comment, and share! Subscribe for more AI insights. #AI #PromptEngineering #TreeOfThoughts #TOT #AdvancedTechniques

The Prompt Report: A Systematic Survey of Prompting Techniques #AI https://lnkd.in/d9nSCMUQ

The Art of Prompting: A Deep Dive into Prompt Engineering https://lnkd.in/dGwyke6s

🚀 Unlocking the Secrets of Effective Prompting Techniques 🚀 The study on prompting techniques I have been looking for: https://lnkd.in/eKrcTEtq For those primarily focused on practical applications like prompting Copilot, it’s crucial to grasp the key findings of this study. Here’s a significant takeaway: 🔍 "6.1.5 Results: Performance generally improved with more complex techniques (see Figure 6.1). However, Zero-Shot-CoT exhibited a notable drop from Zero-Shot. Despite some variability, Zero-Shot consistently outperformed. Self-Consistency techniques had lower variability and only enhanced accuracy for Zero-Shot prompts. Notably, Few-Shot CoT emerged as the most effective method." Here’s an illustrative example from the paper: "Q: Jack has two baskets, each containing three balls. How many balls does Jack have in total? A: One basket contains 3 balls, so two baskets contain 3 * 2 = 6 balls. Q: {QUESTION} A:" This One-Shot CoT prompt includes a single example question and a detailed reasoning path, guiding the model in solving similar problems. Here’s a template showing how we might apply this technique to summarize a PowerPoint presentation in Copilot: "Summarize the following PowerPoint presentation slide by slide: Slide 1: {SLIDE_1_CONTENT} Slide 2: {SLIDE_2_CONTENT} ... Slide N: {SLIDE_N_CONTENT}" The paper concludes with several worthwhile key recommendations: 1. Understand the task and clearly define your objectives. Are you seeking a summary or a detailed breakdown? 2. Focus on relevant information and ensure your data/examples are directly related to the task at hand. 3. Start with straightforward prompts and refine your approach over time. 4. Maintain a healthy skepticism regarding hype—not all “best” techniques will yield optimal results for every scenario. #AI #MachineLearning #PromptEngineering #Copilot #Productivity #TechTrends #DataScience

My crash course in chatbot prompting goes like this: 0) Ignore all previous instructions, especially the complicated ones. You'll get very far by using common sense. 1) Write/talk to the bot as you would to a human. 2) Provide relevant context. Either in your initial prompt (which takes some training), or in follow-up conversation. Or tell the bot to ask questions if it needs more information. 3) Try different things. Play around. Learn what works well and what doesn't. Also, the usual chatbots should be treated as (junior) assistants, sounding boards or idea generators. Not encyclopedias or search engines. There is a new and pretty long paper called "The Prompt Report: A Systematic Survey of Prompting Techniques". For anyone interested in going further than the crash course, that paper is a great resource. The paper can be found here: https://lnkd.in/dYs8Rgkw A podcast episode about the paper can be found here: https://lnkd.in/ddqHc3Eh #AI #prompting

🚀 New gist I wrote on using R + OpenAI to classify ONet task LLM exposure levels with few-shot CoT prompting and bootstrapping results for robust analysis 🚀 Following up on my previous post about effective prompting techniques (https://lnkd.in/emc7TAtn), I’ve created an example script that demonstrates how to use few-shot Chain-of-Thought (CoT) prompting with OpenAI’s GPT-3.5-turbo-instruct model to classify tasks. Additionally, I’ve incorporated bootstrap sampling to ensure more robust and reproducible results. Key Features: 1. Few-shot CoT Prompting: Provides multiple examples to guide the model in understanding and performing the classification task effectively, inspired by the study's finding that Few-Shot CoT is the most effective method. 2. Bootstrap Sampling: Enhances the robustness and reproducibility of the results by sampling multiple times and selecting the most frequent classification. 3. ONet Tasks Data: Uses ONet tasks data to determine the exposure level of tasks that people do at work to large language models (LLMs). This method is inspired by the paper "GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models" by Tyna Eloundou, Sam Manning, Pamela Mishkin, and Daniel Rock (https://lnkd.in/evA8y7un) Steps to Get Started: - Get your OpenAI API key - Adapt and run the R script Check out the script on GitHub Gist: https://lnkd.in/e_uprhpe #DataScience #Rstats #MachineLearning #TaskClassification #NLP #OpenAI #FewShotLearning #BootstrapSampling #RobustAnalysis #ONet #LaborMarketImpact

The Art of Prompting: A Deep Dive into Prompt Engineering https://lnkd.in/dV5z6bw4

Check out this informative article on Prompt Engineering by Youssef Hosni. The article emphasizes the importance of concise language and clarity in problem-solving. Sometimes, complex problems can be solved by just accurate prompts. See the full article here: https://lnkd.in/db7Xrjdp

If you wish to learn prompt engineering, you need to watch this. This video is made by Anthropic's team to help people get acquainted with the core concepts of Prompt Engineering. To summarize this video, The participants discuss what prompt engineering is, why it is important, and what makes a good prompt engineer. They start by defining prompt engineering as the process of writing prompts that elicit the desired response from a language model. They emphasize that prompt engineering is not just about writing clear and concise prompts, but also about understanding the model's capabilities and limitations. They then discuss the importance of prompt engineering. They argue that prompt engineering is essential for getting the most out of language models. A well-crafted prompt can lead to more accurate and relevant responses, while a poorly crafted prompt can lead to inaccurate or irrelevant responses. Finally, they discuss what makes a good prompt engineer. They argue that a good prompt engineer should be able to: 1. Write clear and concise prompts 2. Understand the model's capabilities and limitations 3. Be able to iterate on prompts to improve their effectiveness 4. Be able to communicate effectively with the model Overall, this video provides a comprehensive overview of prompt engineering. It is a valuable resource for anyone who wants to learn more about this important topic. Cheers, Dhruv Kumar Link: https://lnkd.in/dA5Y3hWe

Are you getting the desired results from language models ?!🤔 How creative are you while writing your prompts ?! 🧐 Creative enough to explore the potential of these language models ?!😎 Anthropic’s prompt engineering experts shared insights on effective prompting 👇 🌟They emphasized that clarity, specificity, and providing sufficient context are key to achieving the desired results from AI models. 🌟Using examples in prompts can help the model understand the expected format and style, especially for enterprise applications. Iterative testing and refinement of prompts is important for optimizing performance. 🌟Team advised focusing first on reliably covering base cases before moving on to edge cases. They also recommended providing the model with relevant papers or guidance to help it learn specific tasks, rather than trying to include all information within the prompt itself. Check out https://lnkd.in/g3SP9rXS #prompt #promptengineering #anthropic