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快思邏輯西施電飯煲（1.0公升）1. 01. 0 5. 5 1. 01. 0 5. 5 O 2410 O O O 640 O O O O O () O O O O O O 1 4 1 60 24 O 1. 01. 0 5. 5 24 24 LED 640
- 容量：1.0公升（米量：1.0-5.5杯）
- 遠紅外線內層黑厚鍋
- 採用快思邏輯技術
| 電飯煲 | ||
| 容量 | 1.0公升（1.0-5.5 杯） | |
| 電磁導熱 | --- | |
| 快思邏輯 | O | |
| 保濕裝置 | 美味感應裝置 | |
| 預校掣 | 24小時（以10分鐘為單位） | |
| 快速煮飯 | O | |
| 冷飯加熱 | --- | |
| 內膽 | 遠紅外線內層黑厚鍋 | |
| 內蓋 | 可拆式 | |
| 液晶顯示屏 | O | |
| 清洗功能 | --- | |
| 煮飯完成時鳴響 | O | |
| 顏色 | 白 | |
| 耗電量 | ||
| 煮飯時 | 640瓦特 | |
| 味道食譜 | ||
| 白米飯 | O | |
| 茉莉香米 | O | |
| 煲仔飯 | --- | |
| 快速煮飯 | O | |
| 糯米飯 | O | |
| 壽司飯 | --- | |
| 糙米飯 | O | |
| 粥(稀飯) | O | |
| 什穀米 | O | |
| 蒸 | O | |
| 蛋糕 | O | |
| 麵包 | O | |
| 炆煮 | --- | |
| 意大利飯 | --- | |
| 即食麵 | --- | |
| 乳酪 | --- | |
| 湯 | O | |
| 烹調時間設定 | ||
| 煮粥時間設定 | 1-4小時 | |
| 蒸煮時間設定 | 1-60分鐘 | |
| 保溫 | ||
| 時間 | 24小時 | |
| 表示已保溫時間 | O | |
- 容量：1.0公升（米量：1.0-5.5杯）
- 遠紅外線內層黑厚鍋
- 採用快思邏輯技術
- 烹調程式：白米（標準、稍軟、稍硬、快速煮飯）、粥、糙米、什穀米、糯米、茉莉米、藜麥、砂鍋料理、蛋糕／麵包、湯／慢煮及蒸煮
- 24小時烹調時間預約
- 24小時保溫功能
- 可拆式內蓋及蒸氣口，方便清洗
- LED顯示屏
- 附件：蒸籃、飯勺、飯勺架、粥勺及量杯
- 640瓦特
- 顏色：白
- 產地：中國
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4.7 ★★★★★
Based on 16 reviews
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Product Reviews
★★★★★ 1
Light on substance and heavy on flaws
Format: Paperback
The book has a great list of topics, but fails to provide much substance any of them.
Most of the provided code is just comments that avoid the actual crux of the issues being discussed. (e.g. #implement the logic to validate XYZ - while the whole point of this chapter is teach how the heck we validate XYZ!)
Some parts are plain wrong, for example the part on Graph based RAG is fundamentally flawed as it assumes the text embedding and the graph embedding are in the same latent space. (This is one of many more examples).
Seems like the book was rushed, and the author has limited hands on experience (if any).
At least we know based on the amount of flaws that it was not written by an LLM
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on December 31, 2025
★★★★★ 5
Excellent book to truly understand LLM design patterns
Format: Paperback
I just finished reviewing Ken Huang's pocket book on LLM Design Patterns, and WOW what an amazing resource! This book is excellent if you want to truly understand how to create and enhance intelligent AI language models, all that in your pocket!
Ken makes the difficult things seem surprisingly easy, and that's the real MAGIC.
- How to prepare your data for training by making it extremely clean.
Developing the brains: the practical aspects of training, optimizing, and maintaining your models.
- Learn amazing prompting techniques (such as Chain-of-Thought and Tree-of-Thoughts) to improve your AI's reasoning and problem-solving abilities.
Learn everything there is to know about RAGs so that your LLM can incorporate outside expertise.
- It also delves into creating "agentic" AI that is capable of action and planning (not only simple plan and execute but also enhanced techniques like ReWoo!)
Really, this feels like a useful toolkit, so Ken thank you for that resource
Thanks, Idan Habler
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on June 9, 2025
★★★★★ 3
A Broad Overview, But Light on Modern Fine-Tuning
Format: Paperback
I'm currently really interested in fine-tuning LLMs and recently completed my first LoRA-based fine-tuning on a quantized model. I came to this book looking for more detail on fine-tuning. While it touches on the topic, I found the content didn’t quite align with the current state of the field in 2025. Techniques like LoRA, QLoRA, and PEFT weren’t really covered, and the material leaned more toward what I think are older or lower level approaches. That made it harder to connect with what I’m actually working on.
That said, when I shifted to other chapters — like the sections on prompt engineering techniques such as Chain of Thought (CoT) and Tree of Thought (ToT) — I found more value. These sections were clearer, and I picked up a few practical insights, like using few-shot examples that walk through the CoT reasoning process. That’s not something I’ve tried before, and I can see how it might help smaller models that struggle with any type of reasoning tasks.
Overall, the book feels more like a broad overview of all LLM concepts. For someone exploring many topics across the LLM ecosystem, it offers a wide-ranging introduction. But for readers like me who are actively trying to learn and apply techniques like fine-tuning and quantization, it may leave you wanting up-to-date guidance.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on August 10, 2025
★★★★★ 5
Great foundation read for security!
Format: Paperback
This book is a great read! It builds a strong foundation and I would highly recommend it for builders who are interetsed in building on LLMs and ensuring everything is secure. Security is super important and this book does it justice!
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Reviewed in the United States on June 27, 2025
★★★★★ 5
Loved it
Format: Paperback
I’ve easily read dozens of tech books. I liked this one a lot. Sure, there were boring parts, but most of it was engaging, especially on dry subjects. I previously read “How AI Works” and found this more informative and way more enjoyable. I got through the 700 pages in about 5 weeks while also learning about probability and linear algebra from other books and online sources. I’d love to read something more advanced by the author, maybe getting into more modern applications. I feel more comfortable with the subject and feel I am now ready to conquer more advanced texts. I initially picked this up to give me some background before reading “How to Build a LLM (from scratch)”. I’ve ordered an intermediary Deep Learning with Python book as well, but wouldn’t mind a more advanced theory book to accompany these books. I’ll definitely be rereading sections of this book to further familiarize myself with topics like backpropagation. Highly recommend if you’re looking for a gentle, but broad introduction to the topic.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on November 14, 2025
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