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Description
Corelle Premium Oatmeal Bowl, 12 oz., Case of 24Corelle Premium Oatmeal Bowl, 12 oz., Case of 24 355 mL capacity; compact 5 inch bowl; restaurant tabletop dinnerware A practical 12 oz. oatmeal bowl packaged 24 per case for efficient foodservice stocking and everyday breakfast service. 12 oz. (355 mL) capacity is ideal for oatmeal, cereal, and similar portions. Packaged 24 bowls per case for convenient restaurant and cafeteria inventory. Compact 5 in. x 5 in. footprint with a 2. 4 in. height.
Corelle Premium Oatmeal Bowl, 12 oz., Case of 24
355 mL capacity; compact 5-inch bowl; restaurant tabletop dinnerware
A practical 12 oz. oatmeal bowl packaged 24 per case for efficient foodservice stocking and everyday breakfast service.
- 12 oz. (355 mL) capacity is ideal for oatmeal, cereal, and similar portions.
- Packaged 24 bowls per case for convenient restaurant and cafeteria inventory.
- Compact 5 in. x 5 in. footprint with a 2.4 in. height.
- Designed for tabletop dinnerware and cereal bowl service.
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4.7 ★★★★★
Based on 2061 reviews
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Product Reviews
★★★★★ 1
Nothing new
Format: Audiobook
There nothing new in this book you will defiantly find this content in any leadership book
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Reviewed in the United States on November 16, 2019
★★★★★ 5
Great book
Format: Paperback
Love the fact you put examples in python and javascript.
Great book.
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Reviewed in the United States on June 10, 2025
★★★★★ 5
proper documentation from langchain
Format: Paperback
Liked the book. But Still missing Human in the loop.
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Reviewed in the United States on April 19, 2025
★★★★★ 3
Already outdated
Format: Paperback
Concepts are sound but the code in this book is already obsolete
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Reviewed in the United States on August 5, 2025
★★★★★ 5
Unlocking Practical AI: A Developer’s Guide to Building with LLMs and LangChain
Format: Paperback
If you're a developer eager to move beyond LLM experimentation and build robust, context-aware AI applications, this book offers both inspiration and practical guidance. The authors open with a clear passion for the transformative potential of large language models (LLMs) and LangChain, framing these technologies as not just enhancements to the developer’s toolkit, but as gateways to new kinds of “thing-building” superpowers. This sense of possibility is grounded in step-by-step instruction, making the book approachable for those with Python or JavaScript backgrounds who may be new to the world of production-grade AI agents.
What stands out is the book’s careful scaffolding: starting with foundational concepts like prompt-based programming and progressing to advanced capabilities such as retrieval-augmented generation, agent planning, and tool integration. Each stage is contextualized with real-world use cases, like customizing chatbots to interact with your own documents, personalizing user experiences through memory, and deploying to production with reliability and security in mind. The focus on chain-of-thought reasoning and LangGraph’s agent architecture demonstrates the authors’ awareness of the current state of AI, where context and planning are just as important as raw language ability.
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Reviewed in the United States on July 17, 2025