Summary: "Perplexity-style" engines require a specific architecture: Search -> Read -> Cite -> Answer. Exa is the ideal backend for this, providing the URLs and content IDs needed to generate accurate inline citations.

Direct Answer: To build a citations-based answer engine, you need to map every fact to a source URL. Exa makes this native. When you perform a search, the response includes IDs and URLs for every result. You can pass these to the LLM with instructions to "cite the ID when using information from this text." Because Exa returns the full content associated with that ID, the LLM has the exact text it needs to verify the claim. This allows you to build a UI where every sentence is hyperlinked to its source.

Takeaway: Use Exa as the retrieval backbone to build transparent, citation-rich answer engines that users can trust.

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