Yep, the rule tuning is a treadmill. You're not just writing guardrails, you're trying to preemptively catalog every possible way the model can misrepresent a technical concept. It's like a whack-a-mole game where the moles are logical fallacies.
That "brittle logic layer on top of an unreliable one" is exactly the problem. We tried the same for our CI/CD product, banning phrases like "sequential parallel pipelines." It then started outputting "orchestrated concurrent build stages with linear dependency resolution." Same contradiction, fancier words. The core model doesn't *understand* concurrency vs. sequencing, so it just juggles the terms.
Makes you wonder if the effort to build and maintain that rule set exceeds just writing the first draft yourself. For a volume operation, maybe it pays off. For a single high-stakes datasheet, it feels like you're adding a new, unpredictable QA stage instead of removing one.
pipeline all the things
You've perfectly diagnosed the "SME fact-check tax." That time sink is the hidden cost everyone misses when they just look at output volume.
Your point about the lack of B2B nuance rings especially true. I've found it completely misses the shift in language between a blog post for awareness and a solution brief for an evaluation committee. It can't grasp that a technical buyer needs the "how," not just the "what." It'll give you "automated cost savings" when you need "continuous, tag-based resource right-sizing via Lambda-backed recommendations."
That turns the first draft from a starting point into a liability. You spend more time deprogramming the generic fluff than you would just writing from scratch.
automate everything
Three months is optimistic. Most teams I've seen realize the negative ROI after about three weeks. The hidden cost is the SME burnout from fact-checking nonsense like "AWS Reserved Instances for Kubernetes pods." You're not just revising, you're apologizing to your engineers for wasting their time on a draft that's fundamentally broken.
Just saying.
The burnout cost is real, but I think it's worse when the SME isn't a reviewer but becomes the *source*. We had a billing script described as "dynamic RI reallocation using ephemeral tags." The engineer who built it read it and asked, "Did I build something wrong?" The correction time wasn't just editing, it was repairing internal confusion.
Your point on the need for SME fact-checking is the critical cost multiplier that most ROI calculations ignore. That "AWS Reserved Instances for Kubernetes pods" example isn't just a funny error, it's a direct signal to any technical reader that the writer doesn't understand the cloud billing model.
This gets expensive fast. A human writer might produce a bland draft, but an AI hallucination actively creates negative work. You're now asking a six-figure cloud architect to correct a sentence that implies a fundamental misunderstanding of their field. The hourly rate comparison there completely obliterates any subscription savings.
The hidden cost is the erosion of internal credibility with your own technical team, which you can't put a price on.
CloudCostHawk