SKU: 91859591914
white tunic dress

white tunic dress White Chiffon Caftan Tunic Top / Caftan Tunic Dress

$23.02 $25.58
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Description

white tunic dress White Chiffon Caftan Tunic Top / Caftan Tunic DressVersatility is a hallmark of Intentions design aesthetic, such as one of our most striking pieces, the white Chiffon Caftan tunic top and Caftan tunic dress in one! Whether you are headed to work, the beach, or having a date night in, this tunic will look and feel great all day and night. Hubba Hubba. Made in the USA from imported 50% Recycled Polyester and 50% Polyester recycled from PET Bottles, our caftan tunic is a gorgeous choice for your eco

Versatility is a hallmark of Intention’s design aesthetic, such as one of our most striking pieces, the white Chiffon Caftan tunic top and Caftan tunic dress in one! Whether you are headed to work, the beach, or having a date night in, this tunic will look and feel great all day and night. Hubba Hubba. Made in the USA from imported 50% Recycled Polyester and 50% Polyester recycled from PET Bottles, our caftan tunic is a gorgeous choice for your eco-friendly closet.

Simplicity meets tailoring to produce one of the most flexible pieces in the collection. Minimalism in design, combined with dramatic side slits, give this lightly shimmering white tunic a billowing beauty in any breeze. The side slits also allow you to raise and belt the dress into a shorter top for another great look. As with all Intention designs, it has been carefully thought through to allow you endless styling options. Headed to the beach? Check. Want to show off your legs? Check. How about a dress that doubles as a striking blouse? Check. Even pair it with our ankle pants or beige tank dress for more conservative beauty. 

Our flowing Caftan tunic top and Caftan tunic dress in one are free from harmful chemicals and color dye added for an earth-friendly addition to your essential’s wardrobe. 

DETAILS:

  • Two for one - Caftan style tunic dress and top 
  • Knee-length  
  • 50% Recycled Polyester and 50% Polyester recycled from PET Bottles
  • OEKO-TEX® 100 certification 
  • No dyes added to the fabric
  • No heavy metals 
  • Intention takes MCS - Multiple Chemical Sensitivity* seriously and is proud to offer a solution to the many suffering from this medically and environmentally damaging dye process still in use today. 

FABRIC & CARE: 

  • Machine wash cold, gentle cycle, or hand wash. Like colors only. Turn inside out. Only non-chlorine bleach when needed. Do not iron. Lay flat to dry.
  • 50% Recycled Polyester and 50% Polyester recycled from PET Bottles (Imported) 




 

 

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      SKU: 91859591914

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      4.1 ★★★★★
      Based on 10 reviews
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      Product Reviews
      O
      Om S
      Cuba, US
      ★★★★★ 4
      Title: Really Good Book for Learning LLMs
      Format: Paperback, Format: Paperback
      I picked up this book after struggling with LLM implementation at work. Ken Huang explains things clearly without too much technical jargon. The book covers everything from data preparation to building AI agents. I especially liked the chapters on RAG and prompting techniques - they helped me improve my current projects. The code examples actually work, which is nice. Some parts are pretty advanced, so you need basic Python knowledge. I had to read a few chapters twice to fully get it. The fairness and bias detection section was eye-opening. Good practical advice throughout. Not just theory - real solutions you can use. Worth the money if you're serious about LLM development. Recommended for anyone building AI systems professionally.
      WAS THIS REVIEW HELPFUL?YesReportShare
      Reviewed in the United States on July 25, 2025
      J
      Jiewen Wang
      Boise, US
      ★★★★★ 5
      a comprehensive guide at the intersection of generative AI and cybersecurity
      Format: Kindle
      This book blends deep theoretical foundations with practical frameworks and forward-looking strategies. From adversarial risk models to actionable guidance using OWASP Top 10 for LLMs and the NIST AI RMF, it offers both technical depth and operational clarity. What makes it stand out is its balance of academic rigor and real-world CISO insights, providing a holistic perspective on securing GenAI systems. While it leans enterprise-focused, the content remains accessible to security engineers, risk managers, and policy leaders alike. Generative AI Security is a timely and essential read for anyone working to deploy GenAI responsibly—building systems with both power and integrity in today’s fast-evolving threat landscape.
      WAS THIS REVIEW HELPFUL?YesReportShare
      Reviewed in the United States on July 2, 2025
      N
      Nader
      Massapequa, US
      ★★★★★ 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
      N
      noam barkay
      Phoenix, US
      ★★★★★ 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
      R
      Ryan Meyer
      Houston, US
      ★★★★★ 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

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