---
title: "RAG Needs a Map: Using GraphRAG to Retrieve Connected Context"
category: "talks"
date: "2026-06-29"
time: "11:05am-12:05pm"
track: "Track 2"
room: "Track 2"
speakers: ["Nyah Macklin"]
sourceLabels: ["Official conference schedule", "Public YouTube metadata"]
scheduleTrack: "Track 2"
scheduleRoom: "Track 2"
scheduleLabels: ["Track 2", "Track 2", "sponsor", "confirmed"]
---
# 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
- [[nyah-macklin]]

## 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|Nyah Macklin]] — Sr. Developer Advocate, Artificial intelligence at [[neo4j|Neo4j]].

### Topics Covered
- [[agentic-search]]
- [[coding-agents]]

### 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.