Graph RAG: Build Knowledge Graph Powered Retrieval Systems

  • IntermediateLevel

  • 128+Students Enrolled

  • 1 Hr 30 MinsDuration

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About this Course

  • This course teaches Graph RAG systems and explains how knowledge graphs improve retrieval accuracy beyond traditional RAG pipelines.
  • Learn how Graph RAG combines structured relationships with language models to enable deeper reasoning and better context understanding.
  • Build complete pipelines from retrieval to graph-based querying and compare outputs between RAG and Graph RAG systems.
  • Understand evaluation techniques used to measure Graph RAG performance and real-world system reliability.

Course Benefits

  • Understand how Graph RAG systems improve retrieval accuracy by incorporating structured relationships and knowledge graphs.
  • Learn to build complete Graph RAG pipelines that handle complex queries and multi-hop reasoning scenarios.
  • Gain hands-on experience comparing traditional RAG and Graph RAG systems using real-world examples.
  • Develop skills in designing advanced retrieval systems used in enterprise AI applications.
  • Learn evaluation techniques to measure performance of retrieval systems beyond basic similarity search.

Learning Outcomes

Understand Graph RAG

Learn how Graph RAG improves retrieval and reasoning.

Build Graph Pipelines

Build scalable knowledge graph-based retrieval pipelines.

Evaluate Graph RAG

Evaluate and compare Graph RAG and RAG performance.

Who Should Enroll

  • AI engineers looking to build advanced retrieval systems using Graph RAG and knowledge graph pipelines.
  • Data scientists exploring limitations of traditional RAG and learning structured retrieval approaches.
  • Developers building GenAI applications that require deeper reasoning and multi-hop retrieval capabilities.
  • Professionals interested in next-generation retrieval systems beyond vector search and embeddings.

Course Curriculum

Learn Graph RAG systems step-by-step from fundamentals to hands-on implementation. Understand limitations of RAG, build Graph RAG pipelines, compare outputs, and evaluate performance using real-world problem scenarios.

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Learn why Graph RAG systems matter, where traditional RAG breaks, and how knowledge graphs improve retrieval, context understanding, and multi-hop reasoning in advanced AI applications.

  1. 1. Course Introduction

  2. 2. What is RAG and where it breaks

  3. 3. What is Graph RAG and how it fixes those gaps

  4. 4. Components of a Graph RAG system

Work through a real-world problem by building both RAG and Graph RAG systems, comparing outputs, identifying limitations, and evaluating how graph-based retrieval improves reasoning and response quality.

  1. 1. Explaining the problem statement

  2. 2. Hands-on video building A RAG system

  3. 3. Limitation of RAG for this project

  4. 4. Hands-on video building A Graph RAG system and compare with RAG system

  5. 5. Evaluating Graph RAG system

Meet the instructor

Our instructor and mentors carry years of experience in data industry

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Soumil Jain

Data Scientist Trainee

Soumil Jain, Data Scientist Trainee at Analytics Vidhya and AWS Certified Solutions Architect, specializes in Machine Learning, Deep Learning, and Generative AI, passionate about creating impactful, data-driven AI solutions.

Get this Course Now

With this course you’ll get

  • 1 Hour 30 Mins

    Duration

  • Soumil Jain

    Instructor

  • Intermediate

    Level

Certificate of completion

Earn a professional certificate upon course completion

  • Industry-Recognized Credential
  • Career Advancement Credential
  • Shareable Achievement
certificate

Frequently Asked Questions

Looking for answers to other questions?

A Graph RAG system combines retrieval augmented generation with a knowledge graph so that the model can use both retrieved text and structured relationships. This makes it better at understanding entity connections, multi-step reasoning, and questions that require linking information across multiple sources instead of only relying on vector similarity.

Traditional RAG mainly depends on vector search to retrieve similar chunks of text, but it may miss deeper links between entities or events. Graph RAG adds a layer of structured relationships through a knowledge graph, which allows the system to connect information across nodes and provide more coherent, context-rich answers.

Traditional RAG systems often struggle when a question requires multi-hop reasoning, cross-document linking, or entity relationship tracking. They may retrieve relevant chunks individually but fail to connect them logically. This can lead to incomplete, shallow, or even misleading answers when the task demands reasoning beyond simple similarity matching.

A knowledge graph stores entities and the relationships between them in a structured format. In Graph RAG, this helps the retrieval system move beyond isolated document chunks and reason across connected information. As a result, the system can return answers that better reflect how concepts relate to one another in real scenarios.

Graph RAG systems are especially useful for problems where relationships matter as much as the content itself. This includes enterprise search, compliance analysis, research assistants, healthcare knowledge systems, fraud detection, and any application where answering a question may require linking multiple facts, entities, or documents together.

A basic understanding of traditional RAG concepts will be helpful, but this course starts with the foundations before introducing Graph RAG. It explains where normal RAG works well, where it fails, and how graph-based retrieval solves those gaps, making it manageable for learners who already know the basics of retrieval systems.

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