Large Language Models (LLMs) learn and perform based on the information they’re given. For powerful tools like RAG (Retrieval Augmented Generation) LLMs, it’s even more critical that they are trained and fed with accurate, relevant data from our own products and systems to ensure they truly understand and interact effectively with our customers. This session will explore a fundamental question: how do we ensure the data we’re sending to these intelligent LLMs is correct, complete, and unbiased?

We’ll discuss how data often comes from many different sources, needing to be gathered and prepared (a process known as ETL – Extract, Transform, Load). ETL testing plays a crucial role in validating this data, ensuring it is accurate, consistent, and clean before it trains the LLM. This preparation isn’t just about shaping the data for business needs, but also about making sure it meets ethical standards and avoids introducing unfair biases. We will highlight how even small errors or flaws in this data can lead to serious problems for the LLM, such as giving out wrong information, showing unwanted biases, or simply performing poorly. Join us to learn how proper data testing acts as a crucial safeguard, guaranteeing your LLMs are built on a solid foundation of quality data, leading to better customer interactions and more reliable AI