Prompt Engineering Overview

Course Feature

Cost

Free
Provider

Youtube
Certificate

No Information
Language

English
Start Date

2023-02-19
Learners

No Information
Duration

No Information
Instructor

Elvis Saravia

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3.0

142,487 Ratings

Are you interested in learning the basics of prompt engineering and the latest techniques? Join the cohort-based course on Prompt Engineering for LLMs, hosted by Maven and Dair.AI. This course covers the fundamentals of prompt engineering, tools and applications, and provides a conclusion and future directions. With the help of OpenAI, GPT-3, Artificial Intelligence, NLP, ChatGPT, LangChain, and Machine Learning, you will be able to gain a comprehensive understanding of prompt engineering. Enroll now and get started!

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Course Overview

❗The content presented here is sourced directly from Youtube platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [July 18th, 2023]

This course provides an overview of Prompt Engineering, covering the basics and the latest techniques. It is designed for LLMs and is delivered in a cohort-based format in partnership with Maven. Enrollment information can be found at https://maven.com/dair-ai/prompt-engi. The course is divided into four parts: Part 1 - Introduction to Prompt Engineering; Part 2 - Advanced Techniques for Prompt Engineering; Part 3 - Tools and Applications; and Part 4 - Conclusion and Future Directions. The course website is https://www.promptingguide.ai/, and the course notebook, slides, and GitHub repository can be found at https://github.com/dair-ai/Prompt-Eng. Participants are also encouraged to join the Discord server at https://discord.gg/SKgkVT8BGJ. Topics covered include #openai #gpt3 #artificialintelligence #nlp #chatgpt #langchain #machinelearning. A correction to Part 2 - Advanced Techniques for Prompt Engineering is that an example for few-shot prompting can be found at https://github.com/dair-ai/Prompt-Eng.

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Lack of mention of open source models
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