Prompt Engineering for Developers with Google Gemini

Übersicht

Generating useful and reliable outputs from large language models depends heavily on how you prompt them. In this course, Prompt Engineering for Google Gemini, you’ll learn to use the Gemini large language models efficiently through prompt engineering.

First, you’ll explore the basics of prompt engineering. Next, you’ll discover how to call the Gemini models programmatically through their API, as well as the different settings that can be used to drive the most out of these models.

Finally, you’ll learn how to craft prompts for specific use cases. When you’re finished with this course, you’ll have the skills and knowledge needed to get the most optimal results from the Gemini language models.

Lehrplan

• Introduction to Prompt Engineering
Overview of large language models and their capabilities
Importance of prompts in generating outputs
Fundamentals of crafting effective prompts
• Understanding Google Gemini
Introduction to Google Gemini large language models
Key features and specifications of Gemini models
• Accessing Gemini Models Programmatically
Setting up the environment
Introduction to Gemini API
Authentication and API key management
Making API calls to interact with Gemini models
Handling responses and integrating into applications
• Advanced Prompt Engineering Techniques
Exploring prompt formats and structures
Using context and instructions to refine outputs
Experimenting with temperature, max tokens, and other settings
Techniques for debugging and optimizing prompts
• Crafting Prompts for Specific Applications
Common use cases and application scenarios
Designing prompts for text generation tasks
Creating prompts for code generation and data analysis
Customizing prompts for interactive applications
• Best Practices and Ethical Considerations
Ensuring output reliability and accuracy
Maintaining ethical standards in prompt crafting
Addressing biases and content moderation
• Conclusion and Next Steps
Recap of key learning outcomes
Resources for further learning and practice
Course wrap-up and feedback session

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Unterrichtet von

Laurentiu Raducu

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Fachgebiete

Computer Science