The entire pipeline executes in a single call stack. No promises are created, no microtask queue scheduling occurs, and no GC pressure from short-lived async machinery. For CPU-bound workloads like parsing, compression, or transformation of in-memory data, this can be significantly faster than the equivalent Web streams code — which would force async boundaries even when every component is synchronous.
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Once you've identified target queries, the automated system tests them periodically—daily, weekly, or on whatever schedule makes sense for your monitoring needs. Each test queries the AI model with your specified prompt, captures the response, parses which sources were cited, and records whether your content appeared. Over time, this builds a database showing your visibility trends, how often competitors appear for the same queries, and which topics you're gaining or losing ground on.