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WeAreDevelopers LIVE • Apr 15, 2025

Martin O'Hanlon - Make LLMs make sense with GraphRAG

Martin O'hanlon

Martin O'Hanlon argues vector embeddings alone cannot stop AI hallucinations. Discover how GraphRAG acts as your LLM's right brain, injecting deterministic truth into unstructured workflows.

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#1 about 3 min

Hallucinations and factual inaccuracies in generative language models

Generative models frequently fabricate information and present it as undisputed truth when lacking access to private or updated data.

#2 about 5 min

Demonstrating language model hallucinations with custom system prompts

Injecting structured localized data into system prompts constrains conversational outputs and meaningfully reduces model hallucinations.

#3 about 3 min

Enhancing model accuracy with retrieval augmented generation patterns

Pairing language features with robust data retrieval architectures seamlessly grounds generated text in verifiable domain facts.

#4 about 5 min

Structuring connected data using graph database architectural fundamentals

Graph databases optimally organize complex ecosystems using systematically interconnected nodes and explicit relationship properties.

#5 about 5 min

Limitations of vector embeddings for precise factual querying

While vector embeddings streamline fuzzy semantic searches, they consistently struggle to reliably resolve strict arithmetic or fact-oriented constraints.

#6 about 6 min

Integrating knowledge graphs to provide verified application context

Mapping unstructured enterprise information into explicit factual networks empowers language applications to correctly evaluate nuanced logical conditions.

#7 about 2 min

Implementing graph generation and automated contextual querying methodologies

Automated extraction utilities effectively synthesize unstructured text into traversable databases to continuously supply contextual data pipelines.

Matching moments

2:26 min

Enhancing language models with graph retrieval augmented generation

1:44 min

Introduction to generative AI and knowledge graphs

Michael Hunger Michael Hunger · World Congress 2024

2:05 min

Enhancing language models with retrieval-augmented generation

Mary Grygleski Mary Grygleski · LIVE

2:22 min

Unlocking generative AI capabilities using knowledge graphs

Zaid Zaim Zaid Zaim +1 · World Congress 2026 Europe

3:29 min

Mitigating artificial intelligence hallucinations with constraints and context

Jemiah Sius Jemiah Sius · World Congress 2024

1:53 min

Overcoming language model challenges using retrieval-augmented generation

Ashish Sharma · LIVE

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Upcoming sessions on this topic

Open session

World Congress 2026 North America

September 25, 2026 · 14:10–14:40

Stage 2

Store Your AI Agent's Memory and Context in a Knowledge Graph

Guy Korland

CEO of FalkorDB

Guy Korland
Open session

World Congress 2026 North America

September 24, 2026 · 16:10–16:40

Stage 3

Sandboxing the Swarm: Building Secure, Serverless AI Agents with Wasm

Thorsten Hans

Sr. Developer Advocate @ Akamai Technologies

Thorsten Hans
Open session

World Congress 2026 North America

September 25, 2026 · 09:40–10:10

Stage 7

Loop Engineering

Li Yin

CEO

Li Yin
Open session

World Congress 2026 North America

September 23, 2026 · 10:00–17:00

Stage 11

Building Stuff with GenAI - The Open Minded Workshop beyond OpenAI

Andreas Erben

CTO for Applied AI and Metaverse at daenet

Andreas Erben
Open session

World Congress 2026 North America

September 24, 2026 · 13:30–14:00

Stage 3

Understanding LLM Architectures: Inside the Design of Modern Models

Jofia Jose Prakash

Director - AI & Governance at Humanity + AI, Inc

Jofia Jose Prakash
Open session

World Congress 2026 North America

September 24, 2026 · 10:20–10:50

Stage 5

Vector, Graph, or Key Value? Choosing Your Agent's Memory

Elizabeth Fuentes Leone

AWS - Developer Advocate/SDE, GenAI

Elizabeth Fuentes Leone

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