Articulating data sources in a GenAI application is essential for transparency, credibility and user trust. Clearly indicating where the AI derives its knowledge helps users assess the reliability of responses and avoid misinformation.
This is especially important in high stakes factual domains like healthcare, finance or legal guidance where decisions must be based on verified data.
How to use this pattern
Cite credible sources inline: Display sources as footnotes, tooltips, or collapsible links. E.g., NoteBookLM adds citations to its answers and links each answer directly to the part of user’s uploaded documents.
Disclose training data scope clearly: For generative tools (text, images, code), offer a simple explanation of what data the model was trained on and what wasn’t included. E.g., Adobe Firefly discloses that its Generative Fill feature is trained on stock imagery, openly licensed work and public domain content where the copyright has expired.
Provide source-level confidence: In cases where multiple sources contribute, visually differentiate higher-confidence or more authoritative sources.