000			01578nam a22002297a 4500
005			20260709110011.0
008			260709s2024 |||||||| |||| 00| 0 eng d
020			_a9781098153434
040			_cPK-LaUMT
082			_a006.35 _bPHO-P
100	1		_aPhoenix, James _913455
245	1	0	_aPrompt engineering for generative AI : _bfuture-proof inputs for reliable AI outputs at scale / _cJames Phoenix and Mike Taylor
260			_aBeijing : _bO'Reilly, _c2024
300			_axvii, 401 p.
500			_aIndex present
520			_a"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." -- Amazon.com
546			_aEng
650			_aArtificial intelligence Computer programs _98923
650			_aAI-computer program _913456
700	1		_aTaylor, Mike _913457
942			_cBK
999			_c141348 _d141348