Retrieval-Augmented Generation (RAG) and Enterprise AI Systems

Description

• Course ID:
  • IC-805-E-05
• Content: This course focuses on the design and development of modern generative AI applications built on enterprise data. The emphasis is on the architecture, implementation, and secure operation of Retrieval-Augmented Generation (RAG) systems in real-world business environments.
  • Key Topics:
    • Fundamentals of Retrieval-Augmented Generation (RAG)
    • Architecture of modern enterprise GenAI systems
    • Embeddings and semantic search (conceptual and practical perspectives)
    • Vector databases and their applications (e.g., indexing and retrieval principles)
    • Chunking strategies for documents and knowledge bases
    • Building data pipelines for unstructured data
    • Prompt orchestration in RAG systems
    • Combining retrieval and generative AI (grounding LLMs with enterprise knowledge)
    • Evaluation of RAG systems (quality, relevance, and hallucination reduction)
    • Security and privacy considerations (data leakage, prompt injection)
    • Scaling and operating RAG applications in enterprise environments
    • Fundamentals of agent-based extensions (Agentic RAG)
  • Practical Components:
    • Building a simple RAG system using enterprise data
    • Implementing a Q&A application based on a knowledge repository
    • Analyzing and optimizing an existing RAG workflow
    • Evaluating retrieval quality and response quality
• Prerequisites:
  • Required:
    • IC-805-G-03 – LLM Workshop, or
    • IC-805-G-04 – Developing and Integrating AI Systems
  • Recommended:
    • Basic understanding of APIs and data structures
    • Initial experience with software development or AI tools
    • Not suitable for beginners without technical background knowledge.
• Certification Requirement:
  • Without certification option
• Conduction Method:
  • Online (For on-site or in-house training, please send us a request via our contact form (Contact - intellcert). We will get in touch with you shortly thereafter.)
• Language:
  • English
• Target  Audience:
  • Software developers and AI engineers
  • Solution architects and enterprise architects
  • Data engineers with a focus on AI
  • IT project managers leading technical AI initiatives
  • Teams seeking to connect LLMs with enterprise data
  • Participants of IC-805-G-04
• Learning Objectives:
  • Participants will be able to:
    • Explain and apply the architecture of RAG systems
    • Prepare and structure enterprise data for AI applications
    • Utilize semantic search and embedding-based retrieval mechanisms
    • Design and technically evaluate RAG systems
    • Identify and mitigate common issues such as hallucinations and inaccurate retrieval
    • Assess security and privacy risks in generative AI systems
    • Implement or evaluate simple enterprise-grade RAG applications
• Note:
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