Complete Prompt Engineering For AI
 
The proliferation of AI papers published monthly is skyrocketing exponentially, presenting a growing challenge to stay informed. Among the notable developments, the open-source project Stable Diffusion has emerged as the fastest-growing repository in GitHub's history. Similarly, ChatGPT has seen unprecedented growth as a consumer product, attracting 1 million users within a week and reaching 100 million users within a few months.

In this course, you'll delve into:

- Introduction to Prompt Engineering and its significance
- Hands-on experience with leading AI tools such as ChatGPT, GPT-4, Midjourney, GitHub Copilot, DALL-E, and Stable Diffusion
- Understanding the capabilities, limitations, and best practices associated with each AI tool
- Mastery of concepts like tokens, log probabilities, and AI-generated content
- Techniques for generating and refining lists, summaries, and role prompts
- Utilizing AI for sentiment analysis, contextualization, and step-by-step reasoning
- Strategies for surpassing token limits and employing meta-prompting
- Exploring advanced AI applications like inpainting, outpainting, and progressive extraction
- Harnessing AI for real-world projects such as generating SEO blog articles and stock photos
- Delving into advanced tooling for AI engineering, including Langchain and AUTOMATIC1111

This course is tailored for:

- Advanced AI enthusiasts looking to deepen their understanding and leverage Python code for AI applications at scale.
- Developers interested in AI and eager to enhance their ability to achieve reliable outcomes in real-world scenarios.
- AI Engineers committed to staying updated on cutting-edge techniques and advancements shaping the industry.

What you'll learn:

- Understand the strengths and weaknesses of ChatGPT, Midjourney, GitHub Copilot, Stable Diffusion & other significant models.
- Familiarize yourself with the "Five Principles of Prompting," along with common tips & tricks for professional-grade output.
- Apply acquired knowledge to develop new AI products through 15+ real-world projects encompassing both text and image generation use cases.
- Gain insight into Python coding patterns and tooling necessary for running and scaling AI reliably in production, paving the way for a career as an AI Engineer.