From Hallucination to Precision: Why Your AI Needs the Right Data

As AI tools become more deeply embedded in knowledge work, one challenge remains stubbornly unresolved: hallucination. Even the most advanced models can produce confident but false outputs—undermining trust, accuracy, and efficiency. So how can this be prevented?

Join Nina Vorobiev and Nick Quaass on June 24 for the next session in our Data On Stage series, From Hallucination to Precision: Why Your AI Needs the Right Data, where they explore:
- The core techniques that significantly reduce AI hallucinations by grounding model outputs in trusted data.
- Best practices for implementing these techniques effectively, including guidance on data curation, chunking, and retrieval.
- The options available to apply these methods in your own workflow—from standalone tools to customizable integrations into enterprise systems.

Whether you're experimenting with AI or scaling it across your organization, this session will help you navigate the path to more reliable, high-precision outputs. After the presentation you will have the chance to ask questions and get personalized insights during our live Q&A.