Skip to content

Booksellers & Trade Customers: Sign up for online bulk buying at trade.atlanticbooks.com for wholesale discounts

Booksellers: Create Account on our B2B Portal for wholesale discounts

Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs

by James Phoenix
Save 41% Save 41%
Current price ₹4,602.00
Original price ₹7,840.00
Original price ₹7,840.00
Original price ₹7,840.00
(-41%)
₹4,602.00
Current price ₹4,602.00

Imported Edition - Ships in 18-21 Days

Free Shipping in India on orders above Rs. 500

Request Bulk Quantity Quote
+91
Book cover type: Paperback
  • ISBN13: 9781098153434
  • Binding: Paperback
  • Subject: N/A
  • Publisher: O'Reilly Media
  • Publisher Imprint: O'Reilly Media
  • Publication Date:
  • Pages: 396
  • Original Price: USD 79.99
  • Language: English
  • Edition: N/A
  • Item Weight: 667 grams
  • BISAC Subject(s): Artificial Intelligence / Natural Language Processing and Data Science / Neural Networks

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

Phoenix, James: - James Phoenix has a background in building reliable data pipelines for marketing teams, including automation of thousands of recurring marketing tasks. He has taught 40+ Data Science bootcamps for General Assembly.

Taylor, Mike: - Mike Taylor built and ran a 50-person marketing agency, including working on innovation projects with Unilever, Nestle, and Facebook. Over 300,000 people have taken his marketing courses on LinkedIn Learning.

Trusted for over 49 years

Family Owned Company

Secure Payment

All Major Credit Cards/Debit Cards/UPI & More Accepted

New & Authentic Products

India's Largest Distributor

Need Support?

Whatsapp Us