$OPEN The RAG Attribution Module from @OpenLedger feels like an important step toward making AI outputs more trustworthy and transparent.
Honestly, it improves traditional Retrieval-Augmented Generation by not only pulling information from external sources, but also tracking and showing where that information comes from. This makes AI responses less like a black box and more like a verifiable chain of knowledge.
What I like about this approach is that it adds accountability to AI-generated answers. Instead of just receiving information, users can also see the supporting sources behind it, which builds more confidence in the results.
For me, this is especially useful in research and decision-making contexts where accuracy matters. It reduces the need to constantly double-check everything manually because the system already provides built-in traceability.
Overall, I see the RAG Attribution Module as a simple but powerful idea combining generation with citation tracking to make AI not only smarter, but also more transparent and reliable.