This guide will introduce you to:
- How knowledge graphs solve AI’s hallucination and complex reasoning problems
- Modeling relationships: The synergy of textual structure and semantic facts
- Choosing an architecture for AI: tightly coupled GraphRAG vs. decoupled agentic tools
- Choosing the right abstraction level for integrating knowledge graphs
Your key takeaways
Verifiable grounding
Knowledge graphs ground LLM responses in verifiable truth, reducing hallucinations
Hybrid reasoning
Hybrid graphs combine textual structure (for citation) with semantic facts (for reasoning)
Powerful Complexity
GraphRAG offers powerful multi-hop reasoning but with higher complexity
Flexible modularity
Agentic tooling provides modularity and flexibility at the cost of potential latency