Large language models (LLMs) and diffusion models, such as ChatGPT and Stable Diffusion, offer remarkable potential because of their extensive training on public text and images from the internet. These models can significantly help in various tasks, reducing the complexity for developers to address problems that were previously difficult to automate.
The book, "Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs", serves as a complete guide to generative AI, detailing how to apply LLMs and diffusion models effectively. Integrating these models into workflows can be challenging, as developers often struggle to obtain reliable results for automated systems. Authors James Phoenix and Mike Taylor introduce prompt engineering principles to help developers harness AI capabilities more efficiently.
You'll learn about the interaction chain structure of AI models, the transformation of application problems into document completion tasks, and the impact of model architecture on interactions. The book "Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs" also explores practical applications of these principles in natural language processing, text and image generation, and code, empowering you to make AI work effectively for your needs.
Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs Table of Contents:
- The Five Principles of Prompting
- Introduction to Large Language Models for Text Generation
- Standard Practices for Text Generation with ChatGPT
- Advanced Techniques for Text Generation with LangChain
- Vector Databases with FAISS and Pinecone
- Autonomous Agents with Memory and Tools
- Introduction to Diffusion Models for Image Generation
- Standard Practices for Image Generation with Midjourney
- Advanced Techniques for Image Generation with Stable Diffusion
- Building AI-Powered Applications
Who is this course for?
- Developers looking to integrate AI into their workflows
- Data scientists interested in generative AI
- AI enthusiasts seeking practical applications of LLMs and diffusion models
- Professionals aiming to automate tasks using advanced AI techniques
- Anyone wanting to understand prompt engineering principles for reliable AI outputs
