Grounding LLMs in Data: Quantitative & Causal Reasoning

LLMs are remarkably powerful at language and open-world knowledge, but that doesn't necessarily make them reliable for quantitative reasoning or analysis of enterprise data. Join this episode of InsideAnalysis as Eric Kavanagh speaks with Pradeep Ravikumar of WoodWide.ai about bridging that gap.

Learn more about how WoodWide.ai is working to automate the journey from data to intelligence while keeping data teams in the loop. Hear more about the fundamental differences between language models and structured enterprise data, the importance of quantitative and causal reasoning for use cases such as customer churn and fraud detection, and how organizations can leverage the strengths of LLMs without sacrificing the rigor of their existing data models.