Graph RAG Training

Discover how to make the most of the power of Graph RAG, an essential component for developing with LLMs. Gain immediate value by tailoring content to your documents and use cases or using our high-quality training examples. You can also select modules based on your team’s skills, ensuring a customized learning experience.

Master LLM control with both retrieval-based and generative approaches to enhance their ability to produce more accurate, relevant, and contextually informed responses.

Graph RAG Training Registration Form

What You’ll Learn

Your team will dive deep into the latest Graph RAG technologies and their practical applications. Specifically, you will:

  • Understand Graph RAG: Learn the intricacies of different types of Graph RAG and their applications in enhancing LLMs
  • Leverage Knowledge Graphs: Learn how to use Knowledge Graphs with Graph RAG to improve the performance and customization of LLMs
  • Hands-On Implementation: Using GraphDB with Graph RAG to build your own advanced knowledge retrieval systems

Trainees will gain practical experience in quickly delivering LLM-based applications, driving innovation and efficiency in your organization to increase the adoption of AI technologies and drive new business innovations

Course Details

  • Group Size: 5-8 People
  • Duration: 1-2 Days
  • Type: Online paid training
  • Certificate: Yes

Who is the Graph RAG Training for?

This training is ideal for software engineers, data engineers, and data scientists who need a crash course on Large Language Models (LLMs)

Prerequisites:

  • Basic Python coding skills: Required. Familiarity with Python syntax and common libraries is expected
  • Basic knowledge in knowledge representation and SPARQL: Optional. Ontotext trainers can provide an informative review and explanation during training if needed

Agenda

Introduction and Objectives

  • Overview of training goals and importance of Graph RAG in LLM development

NLP Technology and LLMs

  • Evolution of NLP and detailed exploration of LLM strengths, weaknesses, and customization with RAG

Practical Skills Development

  • Hands-on fine-tuning and prompt engineering
  • Integrating Knowledge Graphs with LLMs to enhance performance

Graph RAG Implementation and Use Cases

  • Different types of Graph RAG and their applications
  • Real-world examples of successful LLM and Graph RAG implementations

Hands-On Sessions with GraphDB

  • RAG Mechanisms: Simple RAG implementation
  • Enhanced Graph RAG: Using domain-specific knowledge
  • NLQ: Advanced querying with LangChain
  • Prototyping with Streamlit: Quick application development

Evaluation Techniques

  • Methods for measuring and improving LLM performance