Future-Proof Inputs for Reliable Al Outputs Phoenix, James Primary Author Mike Taylor Additional Author mixed material bibliography Sebastopol O Reily Media 2024 id Indonesia

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xvii, 400 hlm.; 23 cm Large language models (LLMs) and diffusion models such as ChatGPT and DALL-E have unprecedented potential. Having been trained on public text and images on the internet, they can contribute to a wide variety of tasks. And with the barrier to entry greatly reduced. practically any developer can harness Al models to tackle problems previously unsuitable for automation. With this book, you'll gain a solid foundation in generative Al and learn how to apply these models in practice. When integrating LLMs and diffusion models into their workflows. most developers struggle to coax out reliable results for use in automated systems. Authors James Phoenix and Mike Taylor show you how prompt engineering principles will enable you to work effectively with Al in production. This book explains: The five principles of prompting that are transferable across models, and will continue to work in the future Applying generative Al to real-world examples using libraries and frameworks such as LangChain Evaluating OpenAl models such as GPT-4 and DALL-E2 against alternatives, including open-source models, comparing strengths and weaknesses How these principles apply in practice in the domains of NLP, text and image generation, and code Artificial Intelligence 006.3 9781098153434 Perpustakaan STIKes Sumber Waras Perpustakaan STIKes Sumber Waras 006.3 PHO p 260621 Perpustakaan 006.3 PHO p download.jpg.jpg 3203 2026-09-03 10:09:32 2026-09-03 10:09:55 machine generated