Source Awareness

Not all information is created equal, and AI models make this problem worse by presenting everything with the same confident tone. A peer-reviewed study, a blog post, a press release, and a hallucinated statistic all arrive in the same clean prose. Source awareness — the habit of asking "where does this come from?" before accepting any claim — is your first line of defense against confident nonsense.

1. Primary Sources Original data, firsthand accounts, research papers, SEC filings, patent applications. These are the raw material. Always prefer primary sources when they exist — they have not been filtered through someone else's interpretation.
2. Secondary Sources Analysis, journalism, textbooks, industry reports. Someone interpreted primary data and drew conclusions. Valuable for context and synthesis, but always one step removed from the original evidence.
3. Tertiary Sources Encyclopedias, aggregators, summaries of summaries. Useful for orientation — understanding the landscape of a topic — but never sufficient as evidence for a specific claim.

When evaluating any source — human or AI-generated — four factors determine its reliability. Recency: is this information current, or has the landscape shifted? Authority: does the source have demonstrated expertise or access to primary data? Bias: does the source have a financial, political, or ideological incentive to present information a certain way? Corroboration: can you find the same claim from independent sources? A claim that passes all four checks is solid. A claim that fails any one of them needs a caveat.