Detecting Whether an AI Engine Cited Your Source: Regex vs LLM Classification
I tried three approaches to programmatic citation detection. Here is what worked and why the hybrid won.

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I tried three approaches to programmatic citation detection. Here is what worked and why the hybrid won.

A small Node.js tool that parses earned media coverage and scores it on entity clarity, claim sourcing, and structural signals that AI engines use to decide whether to cite a piece.
I built a small citation checker after watching Perplexity favor pages that answer cleanly, expose structure, and fetch fast.

A small Node.js pipeline for keeping one founder attached to one class of AI-era PR questions.

How I stopped shipping content before the registry and claims were verified.

A small state machine and an append-only log made retries boring.

A small CI gate can catch entity drift, bad relationships, and graph collisions before they reach production.

One job decides what to publish. Another job does the publishing. Here is why that separation matters and how I implemented it.

Extracting which sources AI engines actually credit — from raw response text to structured citation events across Perplexity, ChatGPT, Gemini, and Claude
