This course equips learners with cutting-edge AI system design skills, focusing on how Retrieval-Augmented Generation (RAG) — the technique that combines large language models with external knowledge retrieval — evolves into agentic RAG, which can reason, plan and act autonomously with structured knowledge represented by knowledge graphs.

 

If you are a data scientist, AI engineer, technical product manager, or researcher aiming to master advanced AI pipelines that integrate retrieval, reasoning and decision-making, this course gives practical, hands-on exposure to the latest RAG architectures and tools.

 

What You’ll Learn — Programme Structure & Topics

The workshop is structured to give both theory and practical hands-on skills, including:

Core Topics Covered

• Introduction to RAG: RAG fundamentals and persistent challenges in AI retrieval systems.

• Evolution to Agentic RAG: How RAG systems can incorporate reasoning and action planning for autonomous task execution.

• Knowledge Graph Integration: Deploying structured knowledge representations to enhance RAG reasoning and context awareness.

• Tools & Hands-On Labs: Live coding with modern AI frameworks like LangChain, FAISS, OpenAI APIs, and graph databases.

• Design Principles: How to design AI systems that combine structured knowledge and retrieved evidence for reliable intelligent outputs.

 

Disciplines / Specialisations

This workshop is relevant to areas including:

• Artificial Intelligence Engineering

• Machine Learning

• Data Science

• Knowledge Engineering

• Software Development / API Integration

• Technical Product Management

 

Highlights of the Course

• Hands-On Live Coding Sessions: Practical exposure to building RAG and agentic RAG pipelines.
• Integration with Knowledge Graph Concepts: Learn how graphs enhance reasoning and context.
• Practical Tool Experience: Work directly with LangChain, FAISS, Graph DBs and relevant APIs.
• Designed for Career Practice: Ideal for professionals seeking more than theoretical knowledge.

 

Career Outcomes (Where This Skill Helps)

Completing this short course strengthens your ability to:

• Design advanced AI applications that can autonomously perform retrieval + reasoning.
• Work effectively with knowledge graphs & semantic retrieval systems.
• Contribute to AI product teams in roles like
• AI Engineer / AI Developer
• Machine Learning Engineer
• Data Scientist / Knowledge Engineer
• Technical Product Manager – AI systems
• LLM systems architect

 

Latest Updates / Special Requirements

Location note: Though offered by Gisma, the course may be delivered at the London campus rather than the Berlin/Potsdam campus.
Because this is a short course, it does not require full academic enrolment equivalence — for example, no formal degree registration is needed beyond the workshop.