Dvorak Katherine (19 results)

No Binding. Condition: VG+. LL-93. A VG or better original release 8 x 10 still. Photographic Image.

Condition: New.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Condition: As New. Unread book in perfect condition.

Paperback. Condition: New.

Condition: New.

Paperback. Condition: New.

Condition: New.

Condition: As New. Unread book in perfect condition.

paperback. Condition: New. In shrink wrap. Looks like an interesting title.

Hardcover. Condition: Near Fine. No Dust Jacket. Contains chapter notes, a bibliography and an index. Original publisher's orange covers with brown and white spine lettering and text blocks. A nice clean, tight and unmarked book with no interior or exterior markings. This book appears to have been unread. This is an uncommon item. ; Chicago Studies in the History of American Religion Series; 8vo, 8"- 9" tall; 252 pages.…

Paperback. Condition: New.

Taschenbuch. Condition: Neu. Neuware - Build robust LLM-powered apps, chatbots, and agents while mastering AI engineering principles that will help you outlast the tools and the hype.Want to build an LLM-powered app but don't know where to begin With this step-by-step guide, you can master the underlying principles of AI engineering by building an LLM-powered app from the ground up. Tame unpredictable models with prompt and context engineering. Use evals to keep them on track. Give chatbots the knowledge to answer anything a user wants to know. Equip agents with the tools and smarts to actually get the job done. By the end, you'll have the intuition and the confidence to build on top of LLMs in the real world.Fragmented documentation, obsolete tutorials, and frameworks that deliver a prototype but flop in production can make AI engineering feel overwhelming. But it doesn't have to be that way. With real-world code and step-by-step instructions as your guide, you can learn to build robust LLM-powered apps from the ground up while mastering both the howand why of the most crucial underlying concepts.Harness context engineering and retrieval systems to create AI assistants that understand your proprietary data. Create chatbots that answer organization-specific questions and help solve users' issues. Design agents that conduct research, make decisions, and take action in the real world. Level up your prompt engineering and get an LLM to do your bidding---not its own. Use automated evals to keep constant tabs on your app's quality while setting up guardrails to protect your users and organization. And implement observability systems that make it easy to debug your app when things do go wrong.With a systematic approach grounded in the core principles of building AI apps for real users, you'll easily evolve and adapt even as the hype and tools come and go.…

Softcover/Taschenbuch. Condition: Neu. Build robust LLM-powered apps, chatbots, and agents while mastering AI engineering principles that will help you outlast the tools and the hype. Want to build an LLM-powered app but don't know where to begin? With this step-by-step guide, you can master the underlying principles of AI engineering by building an LLM-powered app from the ground up. Tame unpredictable models with prompt and context engineering. Use evals to keep them on track. Give chatbots the knowledge to answer anything a user wants to know. Equip agents with the tools and smarts to actually get the job done. By the end, you'll have the intuition and the confidence to build on top of LLMs in the real world. Fragmented documentation, obsolete tutorials, and frameworks that deliver a prototype but flop in production can make AI engineering feel overwhelming. But it doesn't have to be that way. With real-world code and step-by-step instructions as your guide, you can learn to build robust LLM-powered apps from the ground up while mastering both the how and why of the most crucial underlying concepts. Harness context engineering and retrieval systems to create AI assistants that understand your proprietary data. Create chatbots that answer organization-specific questions and help solve users' issues. Design agents that conduct research, make decisions, and take action in the real world. Level up your prompt engineering and get an LLM to do your bidding---not its own. Use automated evals to keep constant tabs on your app's quality while setting up guardrails to protect your users and organization. And implement observability systems that make it easy to debug your app when things do go wrong. With a systematic approach grounded in the core principles of building AI apps for real users, you'll easily evolve and adapt even as the hype and tools come and go. Sprache: Englisch Gewicht in Gramm: 648.…

Paperback. Condition: New.

Taschenbuch. Condition: Neu. A Common-Sense Guide to AI Engineering | Build Production-Ready LLM Applications | Jay Wengrow | Taschenbuch | Englisch | 2026 | Pragmatic Programmers | EAN 9798888651933 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu. …

Taschenbuch. Condition: Neu. Neuware -Build robust LLM-powered apps, chatbots, and agents while mastering AI engineering principles that will help you outlast the tools and the hype.Want to build an LLM-powered app but don't know where to begin With this step-by-step guide, you can master the underlying principles of AI engineering by building an LLM-powered app from the ground up. Tame unpredictable models with prompt and context engineering. Use evals to keep them on track. Give chatbots the knowledge to answer anything a user wants to know. Equip agents with the tools and smarts to actually get the job done. By the end, you'll have the intuition and the confidence to build on top of LLMs in the real world.Fragmented documentation, obsolete tutorials, and frameworks that deliver a prototype but flop in production can make AI engineering feel overwhelming. But it doesn't have to be that way. With real-world code and step-by-step instructions as your guide, you can learn to build robust LLM-powered apps from the ground up while mastering both the howand why of the most crucial underlying concepts.Harness context engineering and retrieval systems to create AI assistants that understand your proprietary data. Create chatbots that answer organization-specific questions and help solve users' issues. Design agents that conduct research, make decisions, and take action in the real world. Level up your prompt engineering and get an LLM to do your bidding---not its own. Use automated evals to keep constant tabs on your app's quality while setting up guardrails to protect your users and organization. And implement observability systems that make it easy to debug your app when things do go wrong.With a systematic approach grounded in the core principles of building AI apps for real users, you'll easily evolve and adapt even as the hype and tools come and go. 340 pp. Englisch.…

Paperback. Condition: new. Paperback. Build robust LLM-powered apps, chatbots, and agents while mastering AI engineering principles that will help you outlast the tools and the hype.Want to build an LLM-powered app but don't know where to begin?With this step-by-step guide, you can master the underlying principles of AI engineering by building an LLM-powered app from the ground up. Tame unpredictable models with prompt and context engineering. Use evals to keep them on track. Give chatbots the knowledge to answer anything a user wants to know. Equip agents with the tools and smarts to actually get the job done. By the end, you'll have the intuition and the confidence to build on top of LLMs in the real world.Fragmented documentation, obsolete tutorials, and frameworks that deliver a prototype but flop in production can make AI engineering feel overwhelming. But it doesn't have to be that way. With real-world code and step-by-step instructions as your guide, you can learn to build robust LLM-powered apps from the ground up while mastering both the howand why of the most crucial underlying concepts.Harness context engineering and retrieval systems to create AI assistants that understand your proprietary data. Create chatbots that answer organization-specific questions and help solve users' issues. Design agents that conduct research, make decisions, and take action in the real world. Level up your prompt engineering and get an LLM to do your bidding---not its own. Use automated evals to keep constant tabs on your app's quality while setting up guardrails to protect your users and organization. And implement observability systems that make it easy to debug your app when things do go wrong.With a systematic approach grounded in the core principles of building AI apps for real users, you'll easily evolve and adapt even as the hype and tools come and go. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…