Course Introduction
The Knowledge Graphs and GraphRAG for Generative AI Training Course provides a practical and strategic foundation for understanding how knowledge graphs and graph-enhanced retrieval can improve the accuracy, context, relevance, and explainability of generative AI systems. Participants explore how structured relationships between entities, concepts, documents, events, and evidence can provide large language models with richer contextual information and more reliable knowledge for enterprise and research applications.
Generative AI systems can produce fluent and useful responses, but conventional retrieval approaches may struggle when users ask questions requiring multiple relationships, connected evidence, organizational context, or deeper exploration across distributed information sources. This course examines how knowledge graphs can organize complex information into connected structures and how GraphRAG can combine graph retrieval with vector search, semantic retrieval, and large language models to deliver more contextually grounded AI-generated responses.
Participants develop practical knowledge of graph modeling, entities, relationships, ontologies, semantic representations, graph databases, entity resolution, graph construction, retrieval pipelines, vector embeddings, hybrid retrieval, graph traversal, and contextual generation. The course demonstrates how graph-based retrieval can complement traditional RAG by identifying relevant relationships and connected information before passing carefully selected context to generative AI models. Emphasis is placed on designing retrieval workflows that are accurate, efficient, explainable, maintainable, and aligned with enterprise requirements.
The course explores practical applications across enterprise search, knowledge management, research assistance, customer intelligence, technical support, policy analysis, document intelligence, decision support, and AI-powered organizational knowledge systems. Participants examine how knowledge graphs can connect people, products, documents, processes, technologies, projects, policies, research findings, and business concepts. GraphRAG applications are then used to retrieve and reason over these relationships, enabling generative AI systems to respond with greater awareness of organizational context and supporting evidence.
Trust, governance, and responsible AI are central to the program. Participants investigate risks involving hallucinated relationships, incomplete knowledge graphs, inaccurate entity resolution, stale information, unsupported claims, retrieval bias, privacy, access controls, source attribution, and model dependency. The course also addresses emerging developments including multimodal knowledge graphs, dynamic GraphRAG, graph-native AI agents, automated ontology generation, graph machine learning, real-time knowledge retrieval, and increasingly autonomous generative AI systems.
By completing the Knowledge Graphs and GraphRAG for Generative AI Training Course, participants will be prepared to evaluate, design, implement, and govern graph-enhanced generative AI solutions. They will understand how to connect enterprise knowledge with modern language models, combine graph and vector retrieval, improve AI grounding, and develop more trustworthy knowledge-intensive applications. The course also provides a forward-looking perspective on how knowledge graphs and GraphRAG can become foundational components of future enterprise AI architectures.
Duration
5 days
Who Should Attend
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AI architects designing enterprise generative AI solutions, knowledge systems, retrieval architectures, and intelligent information platforms.
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Data architects responsible for knowledge graphs, semantic data models, enterprise data integration, and AI-ready information architectures.
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Data engineers developing pipelines for graph construction, document processing, entity extraction, metadata enrichment, and retrieval systems.
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AI and machine learning professionals building RAG, GraphRAG, large language model, and graph-enhanced AI applications.
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Enterprise application developers integrating generative AI capabilities with organizational knowledge, databases, documents, and business applications.
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Knowledge management professionals seeking advanced approaches for connecting organizational knowledge with generative AI systems.
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Research professionals developing AI-assisted research, evidence discovery, literature analysis, knowledge exploration, and intelligent research workflows.
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Business intelligence professionals exploring knowledge graphs and generative AI for contextual analytics, enterprise search, and decision support.
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Information scientists and librarians working with semantic technologies, knowledge organization, digital repositories, and AI-enabled information discovery.
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Data governance professionals responsible for data quality, provenance, privacy, access control, AI governance, and trustworthy enterprise knowledge.
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Enterprise architects evaluating GraphRAG, knowledge graphs, vector retrieval, semantic search, and generative AI technology strategies.
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Consultants and technology leaders supporting organizations with generative AI adoption, knowledge management, intelligent retrieval, and enterprise AI transformation.
Course Objectives
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Develop a comprehensive understanding of knowledge graphs and GraphRAG and explain how they can improve context, grounding, retrieval quality, and reliability in generative AI systems.
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Design knowledge graph models that accurately represent entities, relationships, concepts, documents, events, evidence, metadata, and organizational context required by generative AI applications.
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Apply graph construction techniques to integrate structured databases, documents, repositories, APIs, research sources, and enterprise information into AI-ready knowledge structures.
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Build hybrid retrieval approaches that combine graph traversal, vector similarity, semantic search, keyword retrieval, metadata filtering, and contextual ranking for generative AI applications.
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Design GraphRAG workflows that provide large language models with relevant relationships, connected evidence, source context, and structured knowledge for complex multi-hop questions.
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Evaluate generative AI responses using measures such as factual accuracy, source grounding, retrieval relevance, relationship correctness, citation quality, completeness, and contextual consistency.
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Identify and mitigate GraphRAG risks involving hallucinations, fabricated relationships, incomplete graph structures, stale information, retrieval bias, entity resolution errors, and unsupported AI-generated claims.
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Establish governance frameworks covering graph provenance, data quality, privacy, permissions, security, source attribution, retrieval transparency, AI oversight, and knowledge lifecycle management.
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Integrate knowledge graphs and GraphRAG with enterprise copilots, AI assistants, semantic search systems, research platforms, recommendation engines, and intelligent decision-support applications.
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Prepare future-ready generative AI strategies incorporating multimodal knowledge graphs, dynamic GraphRAG, graph-based agents, automated knowledge construction, graph reasoning, and trustworthy autonomous AI.
Comprehensive Course Outline
Module 1: Foundations of Knowledge Graphs, GraphRAG, and Generative AI
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Understanding knowledge graphs as connected representations of entities, concepts, relationships, events, documents, evidence, and contextual enterprise information.
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Examining GraphRAG as an architecture that combines knowledge graphs, graph retrieval, vector search, semantic retrieval, and generative AI models.
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Comparing conventional RAG, vector RAG, semantic search, knowledge graphs, GraphRAG, and graph-enhanced generative AI approaches for complex information needs.
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Identifying business, research, and knowledge management scenarios where graph-enhanced generative AI can provide greater contextual understanding and evidence discovery.
Module 2: Knowledge Graph Modeling for Generative AI
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Identifying entities, properties, relationships, events, concepts, documents, sources, and contextual attributes needed to represent organizational knowledge for AI applications.
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Designing graph schemas that preserve semantic meaning, business context, research relationships, temporal information, provenance, and source references.
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Developing ontologies, taxonomies, controlled vocabularies, and semantic models that improve consistency and contextual understanding within generative AI systems.
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Managing evolving graph structures through schema versioning, ontology changes, new relationships, terminology updates, knowledge expansion, and semantic drift controls.
Module 3: Building AI-Ready Knowledge Graphs
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Designing data ingestion pipelines that transform enterprise databases, documents, research publications, reports, APIs, and repositories into graph-compatible knowledge.
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Applying entity extraction, relationship extraction, entity resolution, classification, deduplication, semantic mapping, and metadata enrichment during graph construction.
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Preserving provenance, source identity, timestamps, confidence levels, permissions, document references, and contextual metadata throughout knowledge graph development.
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Establishing repeatable graph construction and maintenance processes that support incremental updates, validation, source changes, error handling, and continuous knowledge expansion.
Module 4: GraphRAG Architecture and Hybrid Retrieval
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Designing GraphRAG pipelines that connect knowledge graphs, vector databases, embedding models, search systems, large language models, and enterprise information sources.
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Combining graph traversal with vector similarity, keyword search, semantic retrieval, metadata filtering, and structured queries to improve retrieval for complex AI questions.
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Selecting relevant graph neighborhoods, documents, entities, relationships, and evidence while minimizing irrelevant context, duplication, retrieval noise, and excessive prompt size.
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Optimizing GraphRAG pipelines for retrieval relevance, contextual coverage, latency, computational efficiency, scalability, cost management, and maintainability.
Module 5: Generative AI Grounding and Context Engineering
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Designing contextual retrieval strategies that provide language models with relevant relationships, source material, business definitions, evidence, and organizational knowledge.
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Applying context engineering principles to determine what information should be retrieved, how it should be structured, and how it should be presented to generative AI models.
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Reducing hallucinations by grounding generated responses in graph relationships, authoritative documents, verified sources, provenance information, and retrieval evidence.
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Evaluating context quality by examining completeness, relevance, redundancy, relationship accuracy, temporal validity, source authority, and alignment with user questions.
Module 6: Enterprise and Research Applications of GraphRAG
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Applying GraphRAG to enterprise search, knowledge management, technical support, customer intelligence, policy analysis, compliance research, and organizational decision support.
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Connecting publications, researchers, projects, datasets, methodologies, institutions, findings, citations, and evidence to support AI-assisted research and knowledge discovery.
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Designing graph-enhanced AI assistants that can explore organizational relationships, retrieve supporting documents, explain connections, and provide context-aware responses.
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Developing application-specific retrieval strategies for complex research questions, multi-hop investigations, comparative analysis, evidence synthesis, and enterprise intelligence workflows.
Module 7: Evaluation, Trust, and Responsible Graph-Enhanced AI
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Establishing evaluation frameworks for retrieval relevance, answer accuracy, factual consistency, source attribution, relationship correctness, completeness, and response grounding.
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Detecting hallucinations, unsupported claims, fabricated relationships, incomplete retrieval, inaccurate graph information, and misleading generated conclusions.
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Evaluating GraphRAG systems using benchmark questions, expert review, automated metrics, source verification, retrieval tracing, and continuous quality monitoring.
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Addressing responsible AI requirements involving privacy, confidential information, intellectual property, access control, bias, inappropriate inference, transparency, and human oversight.
Module 8: Advanced GraphRAG, Graph Machine Learning, and AI Integration
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Exploring graph embeddings, graph neural networks, graph machine learning, similarity models, link prediction, and advanced graph analytics for generative AI applications.
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Integrating graph-enhanced retrieval with enterprise copilots, intelligent search platforms, recommendation systems, analytical assistants, research tools, and AI-powered knowledge applications.
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Examining approaches for combining graph reasoning, vector retrieval, structured queries, semantic search, and large language models within advanced AI architectures.
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Evaluating advanced architectures according to accuracy, interpretability, computational requirements, scalability, model dependency, retrieval performance, and operational complexity.
Module 9: GraphRAG Agents and Intelligent Knowledge Workflows
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Designing AI agents that use knowledge graphs to navigate relationships, retrieve evidence, investigate questions, reason over connected information, and execute multi-step knowledge tasks.
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Combining planning, graph traversal, retrieval, tool use, source validation, and generative reasoning to support complex enterprise and research workflows.
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Establishing human-in-the-loop controls for agentic GraphRAG systems involving approval processes, evidence review, confidence assessment, access management, and exception handling.
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Evaluating autonomous GraphRAG workflows for reliability, traceability, retrieval consistency, reasoning quality, security, recovery from errors, and responsible decision support.
Module 10: Emerging Trends and the Future of Knowledge Graphs and GraphRAG
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Exploring multimodal knowledge graphs and GraphRAG systems that connect text, images, tables, diagrams, audio, video, datasets, and other enterprise information types.
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Examining dynamic GraphRAG architectures that continuously update knowledge as organizational information, research findings, policies, events, and operational data evolve.
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Assessing emerging graph-native AI agents, automated ontology generation, self-updating knowledge graphs, graph reasoning, real-time retrieval, and AI-powered knowledge construction.
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Preparing organizations for challenges involving graph hallucinations, knowledge freshness, semantic drift, data sovereignty, autonomous retrieval, AI governance, interoperability, and trustworthy generative AI.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
