RAG to GraphRAG: How AI Systems Move from Retrieval to Reasoning
Details
We’re back with another AI Meetup by Jeavio!
This time, we’re talking about something every LLM struggles with at some point: context.
Large Language Models are great at sounding smart.
But without the right context? Let’s just say… they can get “creative.” 👀
This is where Retrieval-Augmented Generation (RAG) helps by giving LLMs access to domain knowledge for more grounded answers.
And then there’s GraphRAG, which takes things further by using knowledge graphs to enable relationship-aware, multi-hop reasoning.
In this session, we’ll explore the concepts, walk through practical examples, and run a live demo comparing vector-based retrieval with graph-powered reasoning.
What You’ll Learn:
- Why LLMs need external knowledge
- How RAG works (conceptually)
- Where traditional RAG falls short
- What GraphRAG is and why it matters
- When to use vector-based retrieval vs. graph-powered approaches in real-world AI systems
Seats are limited, and it’s first-come, first-served. RSVP is mandatory, so be sure to register early!
Once you secure your spot, we’ll send a confirmation email your way.
See you at the meetup! 👋