Training on Knowledge Graphs & Retrieval-Augmented Generation (RAG)

Module 1: Foundations of Knowledge Graphs

• Graph theory basics and semantic relationships

• Ontologies, RDF, triples, and linked data

• Enterprise use cases of knowledge graphs
  Case Study: Eliminating data silos using graph-based architecture
  Practical: Designing a simple domain knowledge graph

Module 2: Semantic Modeling & Ontology Design

• Taxonomies vs ontologies

• Schema design and entity relationships

• Knowledge graph tools and frameworks
  Case Study: Improving search accuracy with structured metadata
  Practical: Building an ontology for a business domain

Module 3: Introduction to Retrieval-Augmented Generation (RAG)

• How RAG enhances Large Language Models

• Embeddings and vector search fundamentals

• RAG architecture components
  Case Study: Reducing AI hallucinations in customer support systems
  Practical: Designing a basic RAG workflow

Module 4: Integrating Knowledge Graphs with RAG

• Combining structured and unstructured data

• Hybrid search strategies

• Vector databases and indexing
  Case Study: Enterprise AI assistant powered by knowledge graphs
  Practical: Creating a RAG pipeline architecture

Module 5: Governance, Evaluation & Optimization

• Data quality and bias mitigation

• Performance evaluation metrics

• Security, compliance, and ethical AI
  Case Study: Managing risks in enterprise AI deployments
  Practical: Developing a governance framework for AI knowledge systems