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Your GenAI system is only as smart as its retrieval layer.
A recent enterprise GenAI survey just confirmed what we’ve been seeing in production: 85% of teams are deploying LLMs 71% are already seeing output risks 99% agree: human oversight is still mandatory Let’s be clear—this isn’t about model tuning. It’s about retrieval failure at the infrastructure level. You can’t generate correct answers if your stack can’t model relationships. You can’t trace decisions if your data lacks structure. You can’t scale trust if your system hallucinates under pressure. Here’s what actually works: Load your structured + unstructured knowledge into a graph database Model entities, relationships, and policies—don’t flatten them Route results into your LLM prompt—clean, fast, explainable That’s how you replace retrieval duct tape with graph-native reasoning. Exploring advanced RAG & GraphRAG? Start here: https://github.com/FalkorDB/GraphRAG-SDK
A recent enterprise GenAI survey just confirmed what we’ve been seeing in production:
- 85% of teams are deploying LLMs
- 71% are already seeing output risks
- 99% agree: human oversight is still mandatory
Let’s be clear—this isn’t about model tuning. It’s about retrieval failure at the infrastructure level.
You can’t generate correct answers if your stack can’t model relationships.
You can’t trace decisions if your data lacks structure.
You can’t scale trust if your system hallucinates under pressure.
Here’s what actually works:
- Load your structured + unstructured knowledge into a graph database
- Model entities, relationships, and policies—don’t flatten them
- Route results into your LLM prompt—clean, fast, explainable
That’s how you replace retrieval duct tape with graph-native reasoning.
Exploring advanced RAG & GraphRAG? Start here: https://github.com/FalkorDB/GraphRAG-SDK
