The cleanup trap: Stop asking RAG to fix bad data

The enterprise tech world has been investing heavily in generative AI, but many projects are failing due to poor data quality rather than technical limitations. When these projects hit roadblocks, the tendency is to blame the AI model, but the real issue often lies in the data that's fed into it. This misallocation of blame leads to wasted resources and perpetuates a cycle of failure. Recognizing and addressing data quality issues upfront could drastically improve the success rate of these initiatives, highlighting the importance of clean data for effective AI deployment.