Grounding generative AI in enterprise truth for real-world impact

In general, the most common challenges that come up relate to data quality and management. Like any data or AI system, the success of RAG relies heavily on how well your data is organized and maintained, and how well you understand what it is and how it relates to your overarching objectives and goals.

In particular, many companies overlook the importance of data hygiene when they start implementing RAG components. The old computer science adage of “garbage in, garbage out” still holds true in the gen AI era; if your data is incomplete, inconsistent, or inaccurate, the information RAG retrieves will be too. For instance, if you have two pieces of data in your dataset that contain varied information for the same query, you might end up getting a different answer every time that question is asked.