Gemini: Prompt Engineering Best Practices
Following best practices when working in prompt engineering allows for you to create effective prompts that maximize and improve results obtained from Gemini. This course will teach you the best practices for creating epic prompts with Google Gemini.
What you'll learn
An LLM like Google Gemini is able to understand and process vast amounts of data, solve problems, and create new and creative content. In this course, Gemini: Prompt Engineering Best Practices, you’ll learn which are the recommended guidelines and tips for creating better prompts, known as best practices.
First, you’ll explore how to use reference text, split tasks into smaller tasks, and how to give the model time to think. Next, you’ll discover how to use temperature and token limit settings. Finally, you’ll learn how to fine-tune prompts for specific applications. When you’re finished with this course, you’ll have the skills and knowledge of prompt engineering needed to create epic prompts following Google Gemini’s best practices.
Table of contents
- Obtaining Better Responses from Gemini 3m
- Understanding Temperature and Its Effect on Responses from the LLM 3m
- Managing Output Length and Prompt Relevance via Token Limits 3m
- Fine-tuning Prompts Using Domain Specific Keywords 3m
- Assessing The Effectiveness of Prompts Based on Model Responses 4m
- A Few Final Words and Thank You! 1m
About the author
Xavier is very passionate about teaching, helping others understand AI, Generative AI, search and Big Data. He is also an entrepreneur, project manager, technical author, trainer, and holds a few certifications with Cloudera, Microsoft, and the Scrum Alliance, along with having been awarted Microsoft MVP. He has spent a great deal of his career working on cutting-edge projects. Throughout multiple projects, he has acquired skills to deal with complex enterprise software solutions, working with c... more