rexhq/prompt-engineering: Tips and tricks for working with Large Language Models like OpenAI's GPT-4.
github.com · 8,656 words · saved by 6 readers
Tips and tricks for working with Large Language Models like OpenAI's GPT-4.
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…
saved by
related reading
- Prompt Engineering Guide | Prompt Engineering Guidepromptingguide.ai
- GitHub - Hannibal046/Awesome-LLM: Awesome-LLM: a curated list of Large Language Modelgithub.com
- Prompt engineering | OpenAI APIplatform.openai.com
- Parsed | Custom, interpretable AI systems that continuously learnparsed.com
- Productizing Large Language Modelsblog.replit.com
- Prompt Engineering Guide | Prompt Engineering Guidepromptingguide.ai
- How To Train Your Pet LLM: Prompt Engineeringtxt.cohere.com
- Prompt Engineering Tips and Tricks with GPT-3 · andrew makes thingsblog.andrewcantino.com
- Prompting best practicesdocs.anthropic.com
- Prompt Engineering | Kagglekaggle.com
- The Foundation Understanding LLMs and Prompt Engineering, and Why It All Mattersleeboonstra.dev
- Natural Language Is an Unnatural Interfacevarunshenoy.substack.com