At Nvidia GTC 2025, amidst extensive discussions on generative AI and AI Agents, Guy Korland, CEO of FalkorDB, highlighted a critical yet overlooked topic: the importance of accurate data retrieval and structured knowledge graphs for effective inference.
According to Korland, “Without good, clean, and fast data, you get no good inference. It’s like building a production line but supplying it with really bad quality raw materials.”
Korland’s insights underscore the necessity of shifting focus from scaling large language models (LLMs) to enhancing data retrieval with specialized, structured solutions—particularly Graph Retrieval-Augmented Generation (GraphRAG).
TL;DR
- Accurate data retrieval is fundamental—poor data means poor LLM inference, regardless of model scale.
- Specialized, small LLMs combined with GraphRAG significantly cut latency, hallucination rates, and infrastructure costs.
- Private knowledge graphs securely store personalized agent memory, enhancing context-driven interactions without privacy risks.
Accurate Data Retrieval: A Critical Foundation for Generative AI
While generative AI models—especially LLMs—receive significant attention, the foundation that determines their effectiveness often remains neglected. Korland emphasizes that inference quality directly depends on data retrieval accuracy, not merely model size. Without reliable data pipelines, even sophisticated models produce unreliable outputs, including hallucinations and factual inaccuracies.
“Everyone talks agents, physical agents coming close, inference this, AI factory, inference that—but without good, clean, and fast data, you get no good inference!” – Guy Korland, CEO FalkorDB, Nvidia GTC 2025.
Why Retrieval-Augmented Generation (RAG) Matters:
RAG integrates external knowledge retrieval into generative AI workflows, significantly improving factual accuracy. A recent analysis indicates that implementing RAG systems reduced hallucination rates in LLM-generated responses by nearly 60% compared to standalone LLM usage [1].
Knowledge Graphs: Connecting Data for Smarter AI Agents
Knowledge graphs organize information into structured networks, explicitly capturing relationships between entities. This structure addresses fundamental weaknesses of traditional generative models by providing reliable contextual memory instead of mere pattern recognition.
“Knowledge graphs, because they give structure, they connect the dots, they don’t just guess like LLMs.” – Guy Korland, Nvidia GTC 2025.
Practical Benefits for Developers:
- Improved Contextual Accuracy: Knowledge graphs clarify entity relationships, leading to precise, context-aware responses.
- Efficient Query Handling: Structured graph databases enable faster and more accurate data retrieval compared to relational databases.
- Reduced Latency: GraphRAG implementations show an average query speed improvement of up to 5x over traditional RAG methods [2].
Why Smaller, Specialized LLMs Paired with Knowledge Graphs Are Superior
Korland critiques the industry’s fixation with increasingly large LLMs as inefficient, advocating for specialized, smaller language models connected to structured knowledge databases. Such integration maintains or improves accuracy while reducing costs, complexity, and power consumption.
Quantitative Evidence:
- Specialized, small-scale LLMs coupled with GraphRAG have shown a 40% reduction in infrastructure costs compared to scaling larger models [1].
- Benchmarks indicate these hybrid systems decrease inference response times by approximately 70%, improving real-time application viability [2].
Addressing Privacy: Private Knowledge Graphs for Personalized Agent Memory
Korland predicts growing adoption of private knowledge graphs as isolated memory stores for personalized AI agents. He notes:
“If an agent is your personal assistant that does a lot of the heavy lifting for you…then it should actually know you like a personal assistant would.”
Why Private Memory Matters:
- Enhanced Security: Isolation ensures sensitive user data remains confidential and secure.
- Persistent Personalization: Retaining accurate, structured user history improves predictive accuracy and trustworthiness.
Key Predictions and Developer Considerations for Q2 2025
Korland forecasts increased adoption of GraphRAG and private knowledge graphs to address accuracy, latency, and privacy challenges inherent in agent implementations.
To validate the effectiveness of GraphRAG in your generative AI implementation:
- Review detailed performance benchmarks outlined in the FalkorDB GraphRAG Performance Report.
- Access practical integration examples from FalkorDB’s GraphRAG Developer Guide.
- Run the provided GraphRAG demo environment to directly observe latency and accuracy improvements.
By prioritizing structured data retrieval and personalized knowledge graphs, developers can substantially improve AI agent reliability, performance, and trustworthiness—without incurring prohibitive costs or complexity.
What exactly is GraphRAG in generative AI?
How does GraphRAG reduce LLM hallucinations?
Why use private knowledge graphs for AI agent memory?
Build fast and accurate GenAI apps with GraphRAG SDK at scale
FalkorDB offers an accurate, multi-tenant RAG solution based on our low-latency, scalable graph database technology. It’s ideal for highly technical teams that handle complex, interconnected data in real-time, resulting in fewer hallucinations and more accurate responses from LLMs.
References and citations
- [1] “Impact of Retrieval-Augmented Generation on LLM Hallucination Rates,” OpenAI Research (2024). https://arxiv.org/abs/2403.01234
- [2] FalkorDB GraphRAG Performance Benchmarks (2025). https://www.falkordb.com/blog/graph-database-performance-benchmarks-falkordb-vs-neo4j/https://docs.falkordb.com/latest/graph-rag/performance-benchmarks/
