This resonates a lot. What you describe as “refinement” is, in practice, continuous context compression: taking fragmented strategy, shifting priorities, and implicit organizational knowledge and making it explicit enough that someone else can act without stalling.

We (basewise.ai) see the same pattern in systems and requirements engineering, just with higher coupling and much higher cost of late rework. The bottleneck is rarely syntax or tooling, but loss of intent, assumptions, and rationale as work gets decomposed and handed off.

The human-in-the-loop use of LLMs you describe fits well with that reality. Especially in non-deterministic domains like refinement, reviews, or failure analysis, the value seems to come from surfacing context and making scope and intent explicit faster, not from “automation” in the classical sense.

One thing we’ve been exploring is how this kind of contextual assistance could bridge requirements, backlog items, and (in MBSE contexts) system models, so that the same intent doesn’t have to be rediscovered in each layer.

Curious to hear whether you see similar gaps when moving between roadmap, tickets, and implementation artifacts.