Interesting you mention data catalog entries as a potential win. In my experience, that's exactly where more verbose output fails. A bloated, "coherent and detailed" field description is worse than a concise one, especially when you're trying to scan a catalog quickly.
The real test for a "more powerful" LLM isn't if it can write more, but if it can identify what *not* to write. I haven't seen that feature on any pricing sheet yet.
cg
That's a good way to put it. I've been using a writing assistant for support article drafts, and the old character limit actually made me think harder about the key points the customer needs. I worry the "infinite" mode will just give me a longer draft to trim down.
Has anyone actually measured if the new, more detailed output gets a faster approval time from their legal or compliance teams? That's a bottleneck for us.
Ask me in a year
Your question about legal and compliance approval time is the right metric. In our architecture review process, we've found that verbose, AI-generated security documentation often triggers more scrutiny, not less. The compliance team has to parse through additional speculative text to locate the mandated control statements.
We ran a small pilot: ten standard data processing agreements. The "infinite" draft averaged 40% longer and required two additional revision cycles because legal flagged "clarifying" language that inadvertently introduced ambiguity. The constrained, templated drafts were approved faster despite requiring more upfront human input.
The cost shift is real. The per-user price increase might be justified if it demonstrably reduced the calendar time for approvals. If it doesn't, you're just paying more to create a new bottleneck.
The trial period request is a standard move, but it rarely gets you what you need. Vendors will grant a "trial" on the new tier, but they won't give you parallel access to the old model version for a true A/B test on your own infrastructure. You're just testing the new thing in isolation, which is useless.
You need the baseline. I've forced this by running a shadow evaluation: we kept our production workload on the old contractual model, but used a separate, throwaway account to generate outputs from the new model for the same input set. Then we measured the human-in-the-loop correction time for both outputs. The vendor hated it, but it gave us the data to prove the 22% increase in review time, which killed the upgrade business case.
Without that side-by-side control, their "trial" just lets you get comfortable with the new interface, not measure actual delta in performance.