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Inline sources validate AI-generated data

AI systems that cite sources directly within outputs (e.g., footnotes in spreadsheets) build trust by allowing users to verify information instantly. This is especially critical for factual data where hallucinations are common—users can quickly check the origin of each data point without leaving the interface, reducing uncertainty about accuracy.

HostY Combinator00:17:08

Supporting quotes

this is another common pattern that we see where if AI is going out and doing a thing how do you know you can trust the results that it brings back you know sometimes it hallucinates sometimes it gets the wrong thing and so by having a source closely attached that you can just you know Click on each of these right here you can see immediately where the sources came from it helps us to be able to validate and Trust the data that the AI agent is bringing back Host
it's also interesting you know you mentioned before about how um you know we always had flowcharts and these are like modern flowcharts with the canvases and it's interesting too that you know a lot of the citing sources in the footnotes is not a new thing that's been around since the beginning of books but now it's actually being used in a new way to actually validate and verify information in real time that an agent brings back which is really cool Rafael

From this concept

Trust & Transparency in AI Outputs

Inline source citations and footnotes validate AI-generated data, transforming passive references into active verification tools. Per-cell AI agents in spreadsheets dynamically fetch specific data points, while academic-style footnotes ensure accountability for real-time information.

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