[download pdf] Prompt Engineering for
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
Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs
Free computer e books for downloading Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs 9781098153434
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
Prompt Engineering: From Words to Art and Copy
Dec 21, 2023 —
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: A Blueprint for AI Excellence
generation, is crucial to obtaining accurate what the function should do, its inputs, or its expected outputs. This is a common issue in generative AI
Prompt Engineering for Generative AI - Mike Taylor
Mike Taylor. Undertitel Future-proof inputs for reliable ai outputs. ISBN 9781098153434. Språk Engelska. Vikt 310 gram. Utgiven 2024-05-29. Förlag O'Reilly
Prompt Engineering for Generative AI: Future-Proof Inputs
Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs · The structure of the interaction chain of your program's AI model and the
Prompt Engineering for Generative AI: Future-Proof Inputs
Kirjailijan Mike Taylor teos Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs saatavilla nyt tuotemuodossa Pehmeäkantinen.
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
Prompt Engineering for Generative AI - Phoenix, James
Future-Proof Inputs for Reliable AI Outputs, Paperback (Paperback), Phoenix, James,
Prompt Engineering for Generative AI | Machine Learning
Aug 8, 2023 —
Future-Proof Inputs for Reliable AI Outputs (Paperback)
Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs (Paperback). By James Phoenix, Mike Taylor. $79.99. Coming soon - preorder now
Future-Proof Inputs for Reliable AI Outputs (Paperback)
Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs Cover Image. By James Phoenix, Mike Taylor. $79.99. Add to Cart Add to Wish
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
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