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Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs by James Phoenix, Mike Taylor

• Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs
• James Phoenix, Mike Taylor
• Page: 422
• Format: pdf, ePub, mobi, fb2
• ISBN: 9781098153434
• Publisher: O'Reilly Media, Incorporated

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Amazon look inside download books Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs 9781098153434 RTF iBook (English Edition) by James Phoenix, Mike Taylor

Large language models (LLMs) and diffusion models such as ChatGPT and Stable Diffusion have unprecedented potential. Because they have been trained on all the public text and images on the internet, they can make useful contributions to a wide variety of tasks. And with the barrier to entry greatly reduced today, practically any developer can harness LLMs and diffusion models to tackle problems previously unsuitable for automation. With this book, you'll gain a solid foundation in generative AI, including how to apply these models in practice. When first integrating LLMs and diffusion models into their workflows, most developers struggle to coax reliable enough results from them to use in automated systems. Authors James Phoenix and Mike Taylor show you how a set of principles called prompt engineering can enable you to work effectively with AI. Learn how to empower AI to work for you. This book explains: The structure of the interaction chain of your program's AI model and the fine-grained steps in between How AI model requests arise from transforming the application problem into a document completion problem in the model training domain The influence of LLM and diffusion model architecture—and how to best interact with it How these principles apply in practice in the domains of natural language processing, text and image generation, and code

Future-Proof Inputs for Reliable AI Outputs (Paperback)
With this book, you'll gain a solid foundation in generative AI, including how to apply these models in practice. When first integrating LLMs and diffusion 
Prompt Engineering for Generative AI (häftad)
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Prompt Engineering for Generative AI
Book cover of Prompt Engineering for Generative AI. Prompt Engineering for Generative AI. Future-Proof Inputs for Reliable AI Outputs. James Phoenix · Mike 
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Prompt Engineering for Generative AI: Future-Proof Inputs
When first integrating LLMs and diffusion models into their workflows, most developers struggle to coax reliable enough results from them to use in automated 
Future-Proof Inputs for Reliable AI Outputs (Paperback)
With this book, you'll gain a solid foundation in generative AI, including how to apply these models in practice. When first integrating LLMs and diffusion 
How can prompt engineering improve generative AI results?
By carefully constructing prompts, we can influence the output of AI models and obtain more informative and accurate responses. Here are some 
Prompt Engineering for Generative AI: Future-Proof Inputs
Item Number. 364927010284 ; Book Title. Prompt Engineering for Generative AI: Future-Proof Inputs for Rel ; Publication Date. 2024-06-25 ; Accurate description.
What is the future of prompt engineering versus fine-tuning?
Prompt engineering is the process where you guide generative artificial intelligence (generative AI) solutions to generate desired outputs. Even 
Prompt Engineering for Generative AI: Future-Proof Inputs
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