Best of Prompt Engineering — 2024

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   Codemotion·1y

   From Junior to Senior Developer with ChatGPT

   ChatGPT and similar AI tools can significantly aid developers by analyzing code, suggesting improvements, writing tests, and more. Their effectiveness depends on clear, specific prompts. While they are not designed to solve new or niche problems independently, they excel in tasks like code contextualization, reviews, and documentation. Tools like GitHub Copilot leverage additional context to provide more relevant suggestions, bridging the gap between junior and senior developer roles.

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   Community Picks·2y

   Prompt Engineering For Developers: 11 Concepts and Examples 🎯🧙‍♂️⚡

   Prompt engineering involves refining inputs to AI models like ChatGPT to get optimal responses. Key techniques include making prompts specific, using active voice, giving models time to think, avoiding prompt injections, and utilizing few-shot and zero-shot prompting. It also involves setting constraints, reducing hallucinations, using delimiters, refining prompts iteratively, testing changes systematically, and asking the model to adopt a persona for better context and relevance.

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   Medium·2y

   Prompt Engineering 101 : Understanding the Basics

   Prompt engineering is the art of crafting effective prompts to interact seamlessly with Large Language Models (LLMs) like ChatGPT. By understanding key components such as instruction, context, input data, and output indicators, one can create high-quality prompts. Various prompting techniques like zero-shot, few-shot, and chain-of-thought prompting can drastically influence the results. Iteratively experimenting with different prompts helps refine the results for better outcomes.

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   Article

   Community Picks·2y

   17 Projects for Teams to Build AI Features 100x Faster

   This post highlights 17 projects that can significantly enhance the productivity of developers working with AI. Notable mentions include Latitude LLM for advanced prompt engineering, LiveKit Agents for building real-time multimodal AI applications, and Julep for creating stateful AI agents. The post also covers platforms such as Open WebUI for offline AI interfaces and Quivr for creating AI 'second brains'. Each project includes installation guides, notable features, and use cases to help teams quickly adopt and integrate AI solutions into their workflow.

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   Article

   portkey·2y

   Using Prompt Chaining for Complex Tasks

   Master prompt chaining to break down complex AI tasks into manageable steps, enhancing the efficiency and accuracy of language model applications. This method offers clear workflow advantages, allowing each prompt to build on previous ones and making each step more manageable. Key benefits include better error handling, modular updates, and improved context retention. Learn to create effective prompt chains and discover tools like Portkey AI for optimizing prompts and managing workflows.

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   Article

   Machine Learning Mastery·1y

   5 Free Courses for Mastering LLMs

   Large Language Models (LLMs) have become a significant breakthrough in AI, excelling in understanding and generating human-like text. This post highlights five free courses to help learners master LLMs. Courses include an introduction by Google, an AI for Educators course by Microsoft, a technical deep dive from Cohere’s LLM University, prompt engineering courses by Anthropic, and a detailed LLM agents course by UC Berkeley and Google DeepMind. These courses cater to a range of learners from beginners to those looking to develop expertise in LLM applications and prompt engineering.

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   Article

   Hacker News·2y

   courses/prompt_engineering_interactive_tutorial at master · anthropics/courses

   This course provides a comprehensive guide to mastering prompt engineering within Claude. It covers the basic structure of good prompts, common failure modes, strengths and weaknesses of Claude, and how to build strong prompts for various use cases. Structured into 9 chapters with exercises and an advanced appendix, it includes practical, hands-on elements with an 'Example Playground' area for experimentation. The tutorial leverages Claude 3 Haiku but references other models like Claude 3 Sonnet and Claude 3 Opus for comparison.

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   Article

   Product Hunt·2y

   Latitude - The open-source prompt engineering platform

   Latitude is an open-source platform designed for prompt engineering, offering tools tailored for developers and the AI community. Established by a group of developers, it has been highly rated by users and features prominently in the field of AI and developer tools since its launch in October 2022.

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   Article

   Machine Learning News·2y

   Anthropic AI Launches a Prompt Engineering Tool that Generates Production-Ready Prompts in the Anthropic Console

   Anthropic AI has launched a prompt engineering tool in the Anthropic Console that generates production-ready prompts, helping users maximize their outputs using generative AI and achieve optimal results.

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    Article

    Uber Engineering·1y

    Introducing the Prompt Engineering Toolkit

    A well-crafted prompt is vital for obtaining accurate outputs from Large Language Models (LLMs). To streamline this process, Uber developed a Prompt Engineering Toolkit that centralizes prompt template creation, management, and evaluation. The toolkit supports context enrichment, batch generation, version control, and safety measures to ensure responsible AI use. It includes a GenAI Playground for prompt exploration and advanced guidance techniques to enhance prompt quality. The toolkit is designed to facilitate LLM usage across development and production stages, offering a robust framework for effective prompt engineering.

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    Article

    UX Planet·2y

    11 Prompt Engineering Tips to Boost Your Skills

    Learn how to write effective AI prompts with these 11 tips, ranging from studying successful prompts and trying harder tasks to making prompts flexible and getting feedback. Boost your AI skills by breaking down complex tasks, understanding AI's reasoning, and starting a prompt library.

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    Article

    GitHub Blog·2y

    5 tips to supercharge your developer career in 2024

    Learn actionable tips to supercharge your developer career in 2024, including becoming a pro at prompt engineering, learning shortcuts and hacks for GitHub, brushing up on soft skills, using AI to secure code, and attending GitHub Universe 2024.

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    Article

    Medium·2y

    The Art of the Prompt: A Look at 26 Prompting Principles

    This post explores the principles of prompt engineering and how they can be used to improve the quality and accuracy of AI-generated responses. It discusses different approaches to prompt design and provides examples of how to optimize prompts for specific use cases.

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    Article

    Towards AI·2y

    An AI Agent to Replace Prompt Engineers

    Learn how to build a multi-agent system that automates the process of transforming simple input prompts into advanced ones using large language models (LLMs). The post walks through the initial idea, modeling and building the solution, testing and troubleshooting, and achieving a stable and optimized system. It highlights the steps involved in creating the advanced prompt generator, details code challenges, and provides links to a GitHub repository and a Hugging Face Space for further exploration.

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    Article

    GoPenAI·2y

    Prompt Engineering Basic Guide

    Prompt engineering is crucial for optimizing interactions with large language models (LLMs). It involves designing prompts that include instructions, context, input data, and output indicators. Various types like zero-shot, few-shot, chain-of-thought, self-consistency, tree of thoughts, graph of thought, and forest of thoughts are used to enhance and direct LLM responses effectively. Understanding these techniques helps in improving the safety and capabilities of LLMs.

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    Article

    freeCodeCamp·2y

    Prompt Engineering Basics – How to Write Effective AI Prompts

    Prompt engineering involves crafting clear, context-rich, and specific input prompts to guide AI models for desired outputs. It's a valuable skill for developers, researchers, and general users to enhance AI-driven tasks such as content creation, technical writing, and customer support. Key elements include clarity, context, constraints, and example usage, enabling efficient communication with AI systems.

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    Article

    portkey·1y

    Claude vs. ChatGPT: Comparison of Two Leading AI Models

    ChatGPT and Claude are two leading AI models offering distinct capabilities. ChatGPT excels in structured and precise tasks, responding best to specific prompts and technical instructions. On the other hand, Claude is designed for natural, fluid interactions, making it ideal for creative and ethical tasks. Understanding their prompting styles and strengths can help optimize responses for various applications.

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    Article

    Exemplar Dev·1y

    Reddit : AI Engineer's Handbook

    Developers are finding the AI Engineer's Handbook to be a valuable resource. It covers essential concepts and skills needed for AI engineering, with a particular focus on prompt engineering.

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    Article

    Towards Data Science·2y

    Create Your Own Meal Planner Using ChatGPT

    Creating a meal planner using ChatGPT involves prompt engineering and integrating the OpenAI API with Python. This guide instructs on how to set up an OpenAI account, request an API key, and install necessary Python packages. Additionally, it provides techniques to steer ChatGPT's responses for personalized meal planning and explains how to parse ChatGPT results into structured formats such as JSON.

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    Article

    AIModels.fyi·2y

    The GPT store is stupid and dead

    The GPT store's concept of selling prompts has faltered due to the 'output2prompt' technique, which can reverse-engineer prompts from AI outputs. This vulnerability highlights the challenges in AI business models, intellectual property, and security. Future AI applications may shift towards dynamic prompting and unique data sets to maintain competitive advantages.

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    Data Science Central·2y

    Your Personal GenAI Innovation Curriculum

    A comprehensive curriculum designed to help individuals leverage Generative AI (GenAI) tools for innovation and personal development. The curriculum offers a structured pathway from foundational knowledge to advanced applications, emphasizing critical thinking, ethical alignment, and strategic prompt engineering. It aims to transform the use of GenAI tools from merely improving productivity to driving significant innovative outcomes in various domains.

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    IBM Technology·2y

    How to Build a Multi-agent AI System

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    LangChain·1y

    Promptim: An experimental library for prompt optimization

    Promptim is an experimental library aimed at improving AI system prompts through automated optimization. By utilizing datasets, custom evaluators, and optional human feedback, Promptim refines prompts to enhance performance. It involves an iterative process where new prompts are generated, evaluated, and retained if they show improvements. Integrating with LangSmith, it offers robust dataset management and evaluation capabilities. While it can expedite prompt engineering and bring rigor to the process, human oversight remains essential for final results.

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    DEV·2y

    Prompt Engineering Fundamentals - Generative AI For Beginners (v1)

    This post discusses generative AI for beginners, specifically focusing on prompt engineering fundamentals. It introduces the Generative AI for Beginners Curriculum and provides resources for building generative AI apps. The post also highlights the importance of prompt engineering in generative AI and explores the core concepts related to prompt usage.

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    Article

    portkey·1y

    Exploring prompt engineering techniques for effective AI outputs

    Prompt engineering has become a specialized skill essential for optimizing AI outputs. Various techniques such as zero-shot, few-shot, chain-of-thought, instruction-based, and role-based prompting improve task performance by structuring prompts precisely. Dynamic optimization, automation, and multi-prompt fusion offer scalable solutions, while meta prompting turns models into prompt engineers, enhancing prompt quality. Advanced methods like graph prompting and generated knowledge prompting address complex, structured tasks. Efficient and context-rich prompts are key to harnessing large language models' full potential.

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