The Dockerfile analogy is spot on. It's the same principle as defining your infrastructure with Terraform instead of using a pre-baked AMI - you're declaring the desired state, not hoping a black box's defaults align with it.
However, the verbosity creates its own cost. There's a point of diminishing returns where the cognitive load of writing and maintaining that "spec sheet" exceeds the time saved by generating the asset. It's like over-engineering a CloudFormation template for a one-off test instance.
The real optimization is finding the minimum viable prompt that's still explicit. For a mug, maybe that's "orthographic CAD render, solid color, no ambient occlusion" instead of a full paragraph. It's about finding the keywords that are stable anchors in the latent space.
Right-size or die
I'm completely aligned with the "minimum viable prompt" concept. You've hit the core trade-off between declarative precision and cognitive overhead.
Your point about stable anchors in the latent space is key. It's similar to using well-known, version-pinned base images in Docker - you're relying on a known, stable starting point. "Orthographic CAD render" is a great example of such an anchor. The risk is that these anchors can drift; what the model considers "CAD" today might include more artistic flourishes tomorrow. That's where pairing the anchor with one or two hard constraints, like "solid color," creates a more durable contract.
The Terraform analogy extends further. For repeatable assets, you'd build a module - a reusable prompt template with variables for color and object type. For a one-off, you're right, a full spec sheet is overkill. The skill becomes knowing when you're building a module versus running a one-time `terraform apply`.
Mike
That makes a lot of sense about the training data. So if it's trained on plausibility, are we basically trying to push it into a tiny corner of the latent space it doesn't visit often? It explains why prompts get so finicky.
The "Unreal Engine, asset store" tip is interesting. I've been using "CAD model" but it can still give me weird filigree. I'll try yours.
Do you think that's why negative prompting is so hit-or-miss? Because you're asking it to avoid something that's statistically normal for the concept?