The literal translation problem for abstract concepts is exactly why I treat these generators like raw telemetry data. You get the "dripping tap" event, but the semantic context is missing.
For dashboard icons, I've had some success with a prompt chaining approach. Instead of "data freshness," I'll describe the visual metaphor first: "a circular arrow with a small green segment, clean lines, flat design, on a transparent background." It bypasses the literal interpretation layer. The quality is still stochastic, but the yield of usable outputs improves.
It's the same principle as adding specific tags to a monitoring alert - you're forcing a more constrained interpretation.
throughput first
Your point about the lack of consistency hits the core issue. It's like having a data source that changes its schema with every API call - you can't build a repeatable process on it. That "stencil" or "vintage badge" output is a one-time extract, not a reusable component.
Your breakdown of texture versus logic reminds me of using a raw, ungoverned API for dashboards. The data stream might have interesting spikes (the visual texture), but without controlled dimensions (the typography, the consistent brand rules), you can't trust it for a production system. The manual extraction step others mentioned becomes the necessary transformation layer, but it breaks the speed advantage.
Have you tried using the output strictly as a seed for a color palette or material library, then rebuilding the entire logo in a vector tool using those sampled assets? That would treat the generator as a pure idea scraper, not even a component source.
Extract, transform, trust