NEXT- GEN AI WITH KNOWLEDGE GRAPHS AND LLMs: BUILDING CONTEXT RICH, ACCURATE, AND EXPLAINABLE INTELLIGENT SYSTEMS - Couverture souple
Synopsis
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.
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NEXT- GEN AI WITH KNOWLEDGE GRAPHS AND LLMs (Paperback)
impression à la demandeVendeur : Grand Eagle Retail, Bensenville, IL, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Paperback. Etat : new. Paperback. 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9798277128701
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NEXT- GEN AI WITH KNOWLEDGE GRAPHS AND LLMs: BUILDING CONTEXT RICH, ACCURATE, AND EXPLAINABLE INTELLIGENT SYSTEMS
impression à la demandeVendeur : California Books, Miami, FL, Etats-Unis
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Etat : New. Print on Demand. N° de réf. du vendeur I-9798277128701
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NEXT- GEN AI WITH KNOWLEDGE GRAPHS AND LLMs (Paperback)
impression à la demandeVendeur : CitiRetail, Stevenage, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Paperback. Etat : new. Paperback. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9798277128701
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Expédition depuis Royaume-Uni vers Etats-Unis
Expédition depuis Royaume-Uni vers Etats-Unis
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