🗣️ Post 1: Prompt Engineering Guide: Guides, papers, and resources for prompt engineering

As posted by: yarapavan  |  🔥 Points: 544

https://github.com/dair-ai/Prompt-Engineering-Guide

💬 Summary

Prompt Engineering Guide Sponsored by Prompt engineering is a relatively new discipline for developing and optimizing prompts to efficiently use language models (LMs) for a wide variety of applications and research topics. Prompt engineering skills help to better understand the capabilities and limitations of large language models (LLMs). Researchers use prompt engineering to improve the capacity of LLMs on a wide range of common and complex tasks such as question answering and arithmetic reasoning. Developers use prompt engineering to design robust and effective prompting techniques that interface with LLMs and other tools. Motivated by the high interest in developing with LLMs, we have created this new prompt engineering guide that contains all the latest papers, learning guides, lectures, references, and...

🗣️ Post 2: Brex’s Prompt Engineering Guide

As posted by: appwiz  |  🔥 Points: 540

https://github.com/brexhq/prompt-engineering

💬 Summary

Brex's Prompt Engineering Guide This guide was created by Brex for internal purposes. It's based on lessons learned from researching and creating Large Language Model (LLM) prompts for production use cases. It covers the history around LLMs as well as strategies, guidelines, and safety recommendations for working with and building programmatic systems on top of large language models, like OpenAI's GPT-4. The examples in this document were generated with a non-deterministic language model and the same examples may give you different results. This is a living document. The state-of-the-art best practices and strategies around LLMs are evolving rapidly every day. Discussion and suggestions for improvements are encouraged. Table of Contents What is a Large Language Model (LLM)? A large language...

🗣️ Post 3: Prompt engineering playbook for programmers

As posted by: vinhnx  |  🔥 Points: 464

https://addyo.substack.com/p/the-prompt-engineering-playbook-for

💬 Summary

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🗣️ Post 4: Prompt engineering vs. blind prompting

As posted by: Anon84  |  🔥 Points: 358

https://mitchellh.com/writing/prompt-engineering-vs-blind-prompting

💬 Summary

April 14, 2023 "Prompt Engineering" emerged from the growth of language models to describe the process of applying prompting to effectively extract information from language models, typically for use in real-world applications. A lot of people who claim to be doing prompt engineering today are actually just blind prompting.1 "Blind Prompting" is a term I am using to describe the method of creating prompts with a crude trial-and-error approach paired with minimal or no testing and a very surface level knowedge of prompting. Blind prompting is not prompt engineering. There is also a lot of skepticism about whether prompt engineering can truly be described as "engineering" or if it's just "witchcraft" spouted by hype-chasers. I think in most cases the...

🗣️ Post 5: Prompt Engine – Microsoft's prompt engineering library

As posted by: mmaia  |  🔥 Points: 309

https://github.com/microsoft/prompt-engine

💬 Summary

Prompt Engine This repo contains an NPM utility library for creating and maintaining prompts for Large Language Models (LLMs). Background LLMs like GPT-3 and Codex have continued to push the bounds of what AI is capable of - they can capably generate language and code, but are also capable of emergent behavior like question answering, summarization, classification and dialog. One of the best techniques for enabling specific behavior out of LLMs is called prompt engineering - crafting inputs that coax the model to produce certain kinds of outputs. Few-shot prompting is the discipline of giving examples of inputs and outputs, such that the model has a reference for the type of output you're looking for. Prompt engineering can be as...

🎯 Final Takeaways

These discussions reveal how developers think about emerging AI trends, tool usage, and practical innovation. Take inspiration from these community insights to level up your own development or prompt workflows.