Prompt engineering essentials: Getting better results from LLMs | Tutorial

Struggling to get useful responses from AI models? This prompt engineering tutorial covers everything developers need to know for effective LLM interactions. Learn how to think about context and tokens, structure your requests, and overcome common prompt issues. Perfect for anyone looking to leverage AI more effectively in their development workflow.

#PromptEngineering #AI #LLM

— CHAPTERS —

00:00 Intro to prompt engineering
00:34 What are LLMs?
01:06 Context, tokens, and limitations
01:53 Understanding hallucinations and limitations
02:25 What is a prompt?
03:14 What is prompt engineering?
03:41 Key components of effective prompting
04:15 Refining a prompt example
05:26 Handling prompt confusion and multi-step tasks
06:17 Token limits and iterative prompting
07:14 Being explicit and avoiding assumptions
07:52 Final recap and takeaways

Want to learn more? Visit:
https://github.blog/ai-and-ml/github-copilot/github-for-beginners-how-to-get-llms-to-do-what-you-want

Stay up-to-date on all things GitHub by connecting with us:

YouTube: https://gh.io/subgithub
Blog: https://github.blog
X: https://twitter.com/github
LinkedIn: https://linkedin.com/company/github
Insider newsletter: https://resources.github.com/newsletter/
Instagram: https://www.instagram.com/github
TikTok: https://www.tiktok.com/@github

About GitHub
It’s where over 100 million developers create, share, and ship the best code possible. It’s a place for anyone, from anywhere, to build anything—it’s where the world builds software. https://github.com