RAG Needs a Map: Using GraphRAG to Retrieve Connected Context
Conference Context
- Date/time: 2026-06-29 · 11:05am-12:05pm
- Track/room: Track 2 · Track 2
- Speaker(s): Nyah Macklin
- Session type/status: sponsor · confirmed
- Track: Track 2
- Room: Track 2
- Session type: sponsor
- Status: confirmed
Session Description
Vector search is good at finding similar text, but real answers often depend on how facts, entities, and documents connect. In this hands-on workshop, you’ll build a GraphRAG workflow that uses relationships to retrieve connected context for more grounded AI responses.
Media Evidence
No related AI Engineer channel video found yet.
Evidence Graph
This evidence graph is generated from currently linked source material: official schedule text, related video pages, cached transcripts, visible slide text, dense/reconstructed slide pages, and AI slide-classification audits.
Media Signals
No linked video, transcript, or slide source has been attached yet.
Agent Reading Notes
Use these signals to refine the synopsis, topic links, people/company context, and method notes. If a source is a related external video rather than an exact official recording, keep it framed as supporting evidence.
Transcript Status
No official session recording transcript was found by exact title match on the AI Engineer YouTube channel during this run.
People
Notes
- Pending transcript synthesis when an official recording or confirmed matching video is available.
Synthesis
Synthesized Breakdown
RAG Needs a Map: Using GraphRAG to Retrieve Connected Context ## Conference Context - Date/time: 2026-06-29 · 11:05am-12:05pm - Track/room: Track 2 · Track 2 - Speaker(s): Nyah Macklin - Session type/status: sponsor · confirmed - Track: Track 2 - Room: Track 2 - Session type: sponsor - Status: confirmed ## Session Description Vector search is good at finding similar text, but real answers often depend on how facts, entities, and documents connect. In this hands-on workshop, you’ll build a GraphRAG workflow that uses relationships to retrieve connected context for more grounded AI responses. ## Media Evidence No related AI Engineer channel video found yet. ## Evidence Graph This evidence graph is generated from currently linked source material: official schedule text, related video pages, cached transcripts, visible slide text, dense/reconstructed slide pages, and AI slide-classification audits.
Speaker And Company Context
- Nyah Macklin — Sr. Developer Advocate, Artificial intelligence at Neo4j.
Topics Covered
Derived Links And Source Material
Novel Concepts / Clever Methods
- No highlighted novel concept has been detected yet.
Evidence Boundary
This synthesis is based on the official schedule and linked source pages. It should be revisited when exact session recordings or transcript-backed secondary sources are available.