Prompt Engineering for Generative AI

Future-Proof Inputs for Reliable AI Outputs

De: James Phoenix, Mike Taylor

Narrado por: Mike Chamberlain

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.

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; and how these principles apply in practice in the domains of natural language processing, text and image generation, and code.

PLEASE NOTE: When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio.

©2024 Saxifrage, LLC and Just Understanding Data LTD (P)2025 Ascent Audio

Not Suited to Audiobook

It is impossible to listen to this. I can only take so much "asterisk asterisk newline". It would have been much better if the prompts were read in plain English.

with is this

I tried but they spend the majority painstakingly reading every line break and punctuation such that there is zero flow and the tedium makes itnunliatenavle, if precise

scenarios discuss were real not theory

scenarios discuss were real not theory. the information is good but the voice might bore listeners