One of the biggest challenges in building AI applications is trust. When a language model generates an answer, users often have no way to verify where the information came from. This becomes a serious problem in real-world applications where accuracy and accountability matter.

This is where citations and grounding play a crucial role in RAG systems. Instead of producing answers without context, the system links each response to the specific documents or passages that were used to generate it. This allows users to verify the information, explore the original sources, and build confidence in the system’s output.

Grounding also helps reduce hallucinations by forcing the model to rely on retrieved evidence rather than speculation.

In this chapter, we will explore how to design RAG systems that produce traceable and trustworthy responses, including techniques for attaching citations, highlighting supporting passages, and ensuring that generated answers remain grounded in reliable data.