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Der Jupiter Reitstiefel ist außen aus weichem Kalbsleder und innen aus stabilem, rutschfestem Grip Leder gefertigt. Anatomisch geformte elastische Einsätze dienen der optimalen Weitenregulierung. Außerdem sorgt eine anatomisch geformte rückwärtige Lederlasche mit Elastik-Einsatz und Logo-Druckknopf für den Schutz des darunter liegenden RV-Anhängers. Zur Verbesserung des Tragekomforts wurde eine elastische Schnürung eingearbeitet. Ein widerstandsfähiger Abriebschutz an der Außenseite und eine abriebfeste, rutschfeste und nicht abfärbende Außensohle sorgen dafür, dass der Stiefel lange gut aussieht. Die vorgeformte Innen- und Mittelsohle bieten Komfort und Stabilität.Um die richtige Größe für Stiefel zu finden, sieh dir bitte das Video in der Galerie an.
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4.7 ★★★★★
Based on 152 reviews
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Product Reviews
★★★★★ 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
★★★★★ 3
Good concepts, but uneven depth
Format: Paperback
Some helpful prompt frameworks, but parts felt repetitive and a few sections stayed too high-level for advanced users. Beginners will likely benefit more than experienced practitioners. If you’re new to prompt engineering it’s a decent starting point; if you’re already building structured prompts daily, it may feel light.
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Reviewed in the United States on January 17, 2026