Inhaltsangabe
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 techniques
- Understand advanced workflows like semantic-aware chunking and multi-query prompting
- Build custom solutions such as chatbots and autonomous agents for specific data challenges
- Continuously evaluate and optimize systems for accuracy, relevance, and performance
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Über die Autorin bzw. den Autor
Dominik is a Machine Learning Engineer who has spent years bringing Machine Learning to life in established industry companies like Siemens and Siemens Energy. His career began with researching and applying traditional ML techniques for Forecasting and Anomaly Detection, and has since shifted toward GenAI use cases. Today, Dominik is leading various initiatives across the organization, leveraging Foundation Models and customized RAG systems to improve and automate existing business processes. When he's not driving digital transformation, Dominik shares his expertise through his popular Medium blog, where he simplifies complex Machine Learning concepts into easy-to-digest pieces.
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Rag With Python Cookbook : Practical Recipes from Data Preprocessing to Llm Agents
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Paperback. Zustand: 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. Bestandsnummer des Verkäufers LU-9798341600560
Neu kaufen
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Rag With Python Cookbook : Practical Recipes from Data Preprocessing to Llm Agents
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Verkäuferbewertung 5 von 5 Sternen
Zustand: As New. Unread book in perfect condition. Bestandsnummer des Verkäufers 52658305
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EUR 58,20
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RAG with Python Cookbook
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Paperback. Zustand: 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. Bestandsnummer des Verkäufers LU-9798341600560
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Rag With Python Cookbook : Practical Recipes from Data Preprocessing to Llm Agents
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Verkäuferbewertung 5 von 5 Sternen
Zustand: New. Bestandsnummer des Verkäufers 52658305-n
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RAG with Python Cookbook
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Paperback. Zustand: 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. Bestandsnummer des Verkäufers LU-9798341600560
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RAG with Python Cookbook
Anbieter: Rarewaves.com UK, London, Vereinigtes Königreich
Verkäuferbewertung 5 von 5 Sternen
Paperback. Zustand: 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. Bestandsnummer des Verkäufers LU-9798341600560
Neu kaufen
EUR 68,54
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