Da: GreatBookPrices, Columbia, MD, U.S.A.
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Da: California Books, Miami, FL, U.S.A.
EUR 58,54
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EUR 60,33
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Aggiungi al carrelloPaperback. Condizione: New. As businesses race to unlock the full potential of large language models (LLMs), a critical challenge has emerged: How do you connect these tools to real-time, external data to solve real-world problems? Retrieval-augmented generation (RAG) is the answer. By combining LLMs with information retrieval, RAG empowers you to build everything from intelligent chatbots to autonomous, task-solving agents.Packed with over 70 practical recipes, this go-to guide tackles a wide range of GenAI applications through structured hands-on learning. Author Dominik Polzer provides the tools you need to design, implement, and optimize RAG systems for your unique use cases. Whether you're working with simple data retrieval or designing cutting-edge autonomous agents, this cookbook will help you stay ahead of the curve.Learn core RAG components including embedding, retrieval, and generation techniquesUnderstand advanced workflows like semantic-aware chunking and multi-query promptingBuild custom solutions such as chatbots and autonomous agents for specific data challengesContinuously evaluate and optimize systems for accuracy, relevance, and performance.
Da: GreatBookPrices, Columbia, MD, U.S.A.
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Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Lingua: Inglese
Editore: O'Reilly Media, 2026
Da: CreativeCenters, Peoria, IL, U.S.A.
paperback. Condizione: New.
EUR 68,03
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Aggiungi al carrelloPaperback. Condizione: New. As businesses race to unlock the full potential of large language models (LLMs), a critical challenge has emerged: How do you connect these tools to real-time, external data to solve real-world problems? Retrieval-augmented generation (RAG) is the answer. By combining LLMs with information retrieval, RAG empowers you to build everything from intelligent chatbots to autonomous, task-solving agents.Packed with over 70 practical recipes, this go-to guide tackles a wide range of GenAI applications through structured hands-on learning. Author Dominik Polzer provides the tools you need to design, implement, and optimize RAG systems for your unique use cases. Whether you're working with simple data retrieval or designing cutting-edge autonomous agents, this cookbook will help you stay ahead of the curve.Learn core RAG components including embedding, retrieval, and generation techniquesUnderstand advanced workflows like semantic-aware chunking and multi-query promptingBuild custom solutions such as chatbots and autonomous agents for specific data challengesContinuously evaluate and optimize systems for accuracy, relevance, and performance.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 65,42
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Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 68,49
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Aggiungi al carrelloCondizione: New.
EUR 68,47
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Aggiungi al carrelloPaperback. Condizione: New. As businesses race to unlock the full potential of large language models (LLMs), a critical challenge has emerged: How do you connect these tools to real-time, external data to solve real-world problems? Retrieval-augmented generation (RAG) is the answer. By combining LLMs with information retrieval, RAG empowers you to build everything from intelligent chatbots to autonomous, task-solving agents.Packed with over 70 practical recipes, this go-to guide tackles a wide range of GenAI applications through structured hands-on learning. Author Dominik Polzer provides the tools you need to design, implement, and optimize RAG systems for your unique use cases. Whether you're working with simple data retrieval or designing cutting-edge autonomous agents, this cookbook will help you stay ahead of the curve.Learn core RAG components including embedding, retrieval, and generation techniquesUnderstand advanced workflows like semantic-aware chunking and multi-query promptingBuild custom solutions such as chatbots and autonomous agents for specific data challengesContinuously evaluate and optimize systems for accuracy, relevance, and performance.
EUR 68,50
Quantità: Più di 20 disponibili
Aggiungi al carrelloPaperback. Condizione: New. As businesses race to unlock the full potential of large language models (LLMs), a critical challenge has emerged: How do you connect these tools to real-time, external data to solve real-world problems? Retrieval-augmented generation (RAG) is the answer. By combining LLMs with information retrieval, RAG empowers you to build everything from intelligent chatbots to autonomous, task-solving agents.Packed with over 70 practical recipes, this go-to guide tackles a wide range of GenAI applications through structured hands-on learning. Author Dominik Polzer provides the tools you need to design, implement, and optimize RAG systems for your unique use cases. Whether you're working with simple data retrieval or designing cutting-edge autonomous agents, this cookbook will help you stay ahead of the curve.Learn core RAG components including embedding, retrieval, and generation techniquesUnderstand advanced workflows like semantic-aware chunking and multi-query promptingBuild custom solutions such as chatbots and autonomous agents for specific data challengesContinuously evaluate and optimize systems for accuracy, relevance, and performance.