"Garbage In, Garbage Out" — this old saying has never been truer in the age of artificial intelligence.
But what really happens when the input data isn’t up to par?
Through a use case focused on creating a sales pipeline analysis agent, discover how data quality makes all the difference:
- Poorly structured or incomplete data leads to model hallucinations and inconsistent answers.
- Clean, reliable, and contextualized data, on the other hand, enables 100% accurate and coherent results.
And to go even further — why not leverage generative AI itself to improve and accelerate data preparation and quality?
