Best of Prompt Engineering — August 2024

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   Article

   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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2. 2
   Article

   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

   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

   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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   Video

   IBM Technology·2y

   How to Build a Multi-agent AI System

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