AI systems can only perform as well as the data behind them, and many organisations underestimate how messy their foundations really are.
In this episode, Senior AI Engineer Ivan Erjavac explains how poor data quality feeds directly into unreliable GenAI outputs, weak retrieval-augmented generation results (RAG), and inconsistent agent behaviour. Find out what good data looks like in practice and how organisations can tighten their foundations with both quick fixes and long-term improvements.
Watch the episode to discover:
- What AI-ready data looks like across documents and databases
- How to spot the data issues that quietly undermine accuracy
- Practical steps to improve your data quality
- How to measure data quality and track AI performance
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