Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs (Paperback)
Phoenix, James, Taylor, Mike
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相關主題
商品描述
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
商品描述(中文翻譯)
大型語言模型(LLMs)和擴散模型(如ChatGPT和Stable Diffusion)具有前所未有的潛力。由於它們是通過對互聯網上的所有公開文本和圖像進行訓練,因此它們可以對各種任務做出有用的貢獻。而且,由於現在進入門檻大大降低,實際上任何開發人員都可以利用LLMs和擴散模型來解決以前不適合自動化的問題。
通過本書,您將建立起生成式人工智能的堅實基礎,包括如何在實踐中應用這些模型。當開發人員首次將LLMs和擴散模型整合到他們的工作流程中時,大多數人都很難從中獲得足夠可靠的結果,以在自動化系統中使用。作者James Phoenix和Mike Taylor向您展示了一套稱為提示工程的原則,使您能夠有效地使用人工智能。
學習如何讓人工智能為您工作。本書解釋了以下內容:
- 您的程序的人工智能模型的交互鏈結結構以及其中的細微步驟
- 如何將應用問題轉化為模型訓練領域中的文檔完成問題,從而產生人工智能模型的請求
- LLM和擴散模型架構的影響,以及如何與之最佳互動
- 這些原則在自然語言處理、文本和圖像生成以及代碼領域中的實際應用