NEXT- GEN AI WITH KNOWLEDGE GRAPHS AND LLMs. Dieser Artikel ist nicht verfügbar.
Sprache: Englisch
Verlag: Amazon Digital Services LLC - Kdp, 2025
- Softcover
- Neu

Anbieter: PBShop.store US, Wood Dale, IL, USAPBShop.store US
Verkäufer/-in mit 5 Sternen
Verkäufer:in bei AbeBooks seit 7. April 2005
Nicht verfügbar
Softcover
Zustand: Neu
EUR 20,10
Artikelbeschreibung des Verkäufers
New Book. Shipped from UK. Established seller since 2000.
Bestandsnummer des Verkäufers L2-9798277128701
- Titel
- NEXT- GEN AI WITH KNOWLEDGE GRAPHS AND LLMs
- Autor
- Leo Hartmann
- Verlag
- Amazon Digital Services LLC - Kdp
- Erscheinungsjahr
- 2025
- Zustand
- New
- Einband
- PAP
- Sprache
- Englisch
- ISBN-13
- 9798277128701
- Artikelgewicht
- 318 Gramm
Next-Gen AI with Knowledge Graphs and LLMs is a comprehensive guide to building intelligent systems that combine symbolic reasoning with modern language models. As AI continues to evolve beyond simple text prediction, the future belongs to systems that understand context, model relationships, manage domain knowledge, and deliver reliable, explainable results. This book shows you how Knowledge Graphs and Large Language Models can be integrated to create powerful hybrid intelligence.
Designed for AI engineers, data scientists, architects, and advanced practitioners, this book explains how structured knowledge, graph processing, and LLM understanding can be combined to achieve accuracy, consistency, traceability, and explainability in production environments. You will learn how to connect language models to knowledge bases, use graph reasoning to correct hallucinations, design retrieval workflows, and build robust pipelines that transform LLMs into trustworthy enterprise tools.
Through detailed explanations and practical insights, the book covers semantic modeling, ontology design, graph storage, query systems, RAG pipelines, hybrid reasoning strategies, agentic orchestration, and knowledge-grounded workflows. It demonstrates how to engineer modern AI systems that support decision-making, automate tasks, and deliver verifiable answers that align with real-world constraints.
Inside, you will learn how to:
Design and structure Knowledge Graphs for enterprise-level intelligence.
Connect LLMs to structured knowledge for context-rich reasoning.
Reduce hallucinations through grounding, constraints, and graph-validated retrieval.
Implement hybrid RAG architectures that leverage symbolic and neural intelligence.
Use graph queries, embeddings, and relationships to strengthen factual accuracy.
Build explainable intelligent systems that reveal their reasoning paths.
Deploy scalable, maintainable, and auditable AI pipelines.
Whether you are building recommendation engines, enterprise assistants, decision-support systems, automated research agents, or high-stakes AI workflows, this book gives you the foundation needed to create systems that are intelligent, reliable, and explainable. It is your guide to the next generation of AI engineering.
Designed for AI engineers, data scientists, architects, and advanced practitioners, this book explains how structured knowledge, graph processing, and LLM understanding can be combined to achieve accuracy, consistency, traceability, and explainability in production environments. You will learn how to connect language models to knowledge bases, use graph reasoning to correct hallucinations, design retrieval workflows, and build robust pipelines that transform LLMs into trustworthy enterprise tools.
Through detailed explanations and practical insights, the book covers semantic modeling, ontology design, graph storage, query systems, RAG pipelines, hybrid reasoning strategies, agentic orchestration, and knowledge-grounded workflows. It demonstrates how to engineer modern AI systems that support decision-making, automate tasks, and deliver verifiable answers that align with real-world constraints.
Inside, you will learn how to:
Design and structure Knowledge Graphs for enterprise-level intelligence.
Connect LLMs to structured knowledge for context-rich reasoning.
Reduce hallucinations through grounding, constraints, and graph-validated retrieval.
Implement hybrid RAG architectures that leverage symbolic and neural intelligence.
Use graph queries, embeddings, and relationships to strengthen factual accuracy.
Build explainable intelligent systems that reveal their reasoning paths.
Deploy scalable, maintainable, and auditable AI pipelines.
Whether you are building recommendation engines, enterprise assistants, decision-support systems, automated research agents, or high-stakes AI workflows, this book gives you the foundation needed to create systems that are intelligent, reliable, and explainable. It is your guide to the next generation of AI engineering.
„Inhaltsangabe“ gehört möglicherweise zu einer anderen Auflage dieses Titels.