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velvet dress for ladies Duntery Plunging V-neck A-line Velvet Maxi Dress With Slit, GrapeFall in love with this velvet long dress! Boasting a sultry plunging V neck, stylish long sleeves, an elegant A line silhouette, a tempting leg slit, daring backless allure, and a sleek zipper design, this gown is a perfect choice for weddings, proms, parties or other important occasions. Product SKU: Denise Silhouette: A Line, With Slit Fabric: Velvet Length: Floor Length Back Details: Backless, Zipper Neckline: Plunging V neck Sleeves: Long Sleeves
Fall in love with this velvet long dress! Boasting a sultry plunging V-neck, stylish long sleeves, an elegant A-line silhouette, a tempting leg slit, daring backless allure, and a sleek zipper design, this gown is a perfect choice for weddings, proms, parties or other important occasions.
- Product SKU: Denise
- Silhouette: A-Line, With Slit
- Fabric: Velvet
- Length: Floor-Length
- Back Details: Backless, Zipper
- Neckline: Plunging V-neck
- Sleeves: Long Sleeves
- Embellishment: Sash
- Built-In Bra:Yes
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4.1 ★★★★★
Based on 1876 reviews
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Product Reviews
★★★★★ 4
Understanding AI prompts as a science
Format: Audiobook
Prompt engineering is the art and science of finding the right words to generate the right responses from artificial intelligence (AI). It's becoming a skill in-demand in today's workplaces. It's also becoming an essential skill for life. To understand prompt engineering, you must understand how AI tools understand their inputs. This book explains that to you in many forms.
It covers prompting as it intersects all the major AI disciplines, like general prompts (as in a ChatBot), fine-tuning, and retrieval-augmented generation (RAG). It also introduces how prompting intersects a couple of image-generation tools.
As a scientist, I appreciate the theoretical approach to these practical matters. Too many people are hacks at AI these days, and any technical understanding can rapidly advance an individual's effectiveness. Prompt engineering is often made fun of as a "soft" topic, but as this book demonstrates, it intersects all the major AI areas. Presumably less garbage in means less garbage out. This book shows how you can make that take place.
As typical for O'Reilly materials, this book appeals to people who desire an intermediate-to-advanced understanding of how AI works. It's not for the casual user. Anyone technical involved in professional knowledge generation using AI can benefit from understanding the dynamics "under the hood." It's a good, though perhaps not ground-breaking, textbook for those of us unable to take a class in the subject. Prompt engineering may be a soft topic to many, but books like this ground the field in the science that can make or break a digital or software product.
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Reviewed in the United States on December 13, 2025
★★★★★ 5
Great book
Format: Paperback
Great book! It is helpful as I work towards my Masters of Science in Applied Artificial Intelligence
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Reviewed in the United States on December 20, 2025
★★★★★ 5
Comprehensive Guide with Practical Insights
Format: Paperback
This is a solid book for understanding the art and science of working with LLMs and other generative AI models. I always struggled with getting the output I was looking for, and wasn't sure how best to "ask" the models for what I wanted. This book did a great job of laying out the strategies and practical guidance to craft the prompts. There were a lot of tips and tricks, but the overall understanding and framework around prompt engineering has been super useful.
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Reviewed in the United States on June 25, 2024
★★★★★ 5
A must read!
Format: Paperback
Hands down, the best book on prompt engineering and implementing LLMs. Really enjoyed Michael and James deep dive. Whether you're technical or not, this book is foundational to a deeper understanding in how to properly explore and implement LLMs.
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Reviewed in the United States on June 25, 2024
★★★★★ 4
Helpful for GPT/LangChain framework
Format: Paperback
I applaud the authors for putting forth a comprehensive introduction in a rapidly evolving space. I absorbed a lot, helpful as I was developing a prototype — using open source methods.
And that’s where I was disappointed. The LLM and examples are highly adapted to OpenAI’s GPT-x and the bulky LangChain framework, something not obvious until you dig in to the book. Sure, this may be where newbie demand was when the authors began writing. But as the open source models and OpenAI alternatives gain speed (e.g. Llama 3.1, Groq, etc.) this book may quickly need an updated and expanded version to stay relevant.
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Reviewed in the United States on August 7, 2024
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