Mastering Graph RAG, Corrective RAG & Citations: Build Production-Ready AI Apps with LLMs |

Unlock the next level of generative AI application development! In this comprehensive video, Unfold Data Science dives deep into three essential concepts for large language model (LLM) powered apps: Graph RAG, Corrective RAG (CRAG), and Citations.

You’ll learn:

The fundamentals and challenges of Retrieval-Augmented Generation (RAG)

Why and how Graph RAG uses knowledge graphs to provide rich, global context for smarter retrievals

What Corrective RAG is, including query decomposition, HyDE, score expansion, and ways to handle low-confidence retrievals

How to implement transparent citation systems to fight hallucinations and build user trust and compliance

Step-by-step walkthroughs with real Python code and a demo app

Best practices to develop production-ready, trustworthy LLM solutions

Whether you’re a data scientist, AI engineer, or tech enthusiast, this tutorial provides actionable methods and real-world code to build more reliable and explainable AI.

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Useful Resources & Follow-up:

How to build your first RAG app in 10 minutes on Unfold Data Science -   • Build Your First RAG App in 10 Minutes | R...  

Building RAG Model using LangChain -    • Building RAG based model using Langchain |...  

Agentic AI course for beginners-    • My Free Agentic AI Course is Live | Agenti...  

For code and full resources, check GitHub link in the video or channel description.

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