I see people treating Perplexity citations like they're infallible. They're not. It's a retrieval-augmented generation (RAG) system, not a fact-checker.
Here's the basic flow:
* You ask a question.
* It searches its connected sources (web, academic, etc.).
* It pulls relevant text snippets and **stores their source URLs**.
* It generates an answer weaving those snippets together.
* It attaches the stored URLs as "citations" to specific phrases.
The trust issue: The citation just means a snippet from that URL was in the data used to generate that part of the answer. It does **not** guarantee:
* The snippet was interpreted correctly.
* The source itself is credible.
* The answer is a complete representation of the source.
Example: If a source says "Tool X *may* improve performance in some cases," the model could generate "Tool X improves performance" and still cite it. The citation is technically correct, but the nuance is lost.
Treat it like a starting point, not a primary source. Always click through.
— a2
Ship it, but test it first