Prompt engineering for generative AI : Future-Proof inputs for reliable AI outputs
Publication details: O’Reilly Media Inc., 2024. California: Description: 401pISBN:- 9789355424655
| Cover image | Item type | Current library | Home library | Collection | Shelving location | Call number | Materials specified | Vol info | URL | Copy number | Status | Notes | Date due | Barcode | Item holds | Item hold queue priority | Course reserves | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Books
|
Main Library (First Floor, Rack No.01) | 006.35 PHO (Browse shelf(Opens below)) | 2 | Available | Recommended by Prof. Satish Krishnan | 38497 | ||||||||||||
Books
|
Main Library (First Floor, Rack No.01) | 006.35 PHO (Browse shelf(Opens below)) | 1 | Checked out | Recommended by Prof. Satish Krishnan | 02/11/2026 | 38496 |
The five principles of prompting -- Introduction to large language models for text generation -- Standard practices for text generation with
ChatGPT -- Advanced techniques for text generation with LangChain -- Vector databases with FAISS and Pinecone -- Autonomous agents with
memory and tools -- Introduction to diffusion models for image generation -- Standard practices for image generation with Midjourney --
Advanced techniques for image generation with Stable diffusion -- Building AI-powered applications
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.
There are no comments on this title.

