Last updated on October 22, 2024
Now that you've mastered the basics of Generative AI and prompt engineering, you're ready to explore more advanced techniques and practical applications. This course will guide you through these new areas, providing resources to help you refine your skills and use AI more effectively. Remember, the key to mastering prompt engineering is continuous experimentation with your own prompts.
Here’s a preview of what lies ahead as you deepen your understanding of Generative AI:
Applications: Learn about practical, real-world uses of prompt engineering and how it can be applied to everyday tasks and complex scenarios.
Intermediate: Dive into moderately complex, research-based techniques to further enhance the performance of your AI models. This module will introduce more nuanced prompt engineering strategies.
Advanced Techniques: Explore advanced prompting techniques that push the boundaries of what generative AI can achieve. These lessons will cover cutting-edge methods in the field.
New Techniques: Stay updated with the latest advancements in prompt engineering. This section focuses on new and emerging techniques for optimizing AI behavior and outcomes.
Agents: Discover how AI agents can automate tasks, interact with environments, and learn through prompts. This module will teach you how to effectively utilize agents in various applications.
Reliability: Learn techniques to improve the reliability and accuracy of your AI models. This module will focus on making AI responses more dependable and aligned with user expectations.
Image Prompting: Delve into text-to-image prompting for models like DALLE and Stable Diffusion, expanding your skills to include visual content generation.
Prompt Hacking: Explore the fascinating world of prompt hacking, where you’ll learn how to manipulate prompts to achieve creative or unexpected outcomes from your AI models.
Tooling: Get hands-on with prompt engineering tools and IDEs designed to make the development and fine-tuning of prompts easier and more efficient.
Retrieval-Augmented Generation (RAG): Understand how RAG integrates external data sources to improve the accuracy and relevance of generated responses.
List of Large Language Models: Explore a comprehensive list of Large Language Models (LLMs), their capabilities, and the best practices for using them in different contexts.
For further support, you can refer to: