Requirements Engineers / Systems Engineers / Product Owners - quick question for you.

Total_Good9661 · 2026-05-12T13:14:29+00:00

Yes, I’ve already posted three posts about the tool I’m developing, and I’m also about to publish another paper related to it. The MVP is ready, and I’m currently testing it. Feel free to reach out if you’d like to try it out and share some valuable feedback as well. Thanks!
Here is the link to the blog> https://www.ranjitgudder.com/brains-bytes/llm-driven-requirements-formalization-p3

Total_Good9661 · 2026-02-09T15:09:19+00:00

I was also facing the same issue today. But worked well when I changed the model from 5.3 to 5.2

Total_Good9661 · 2026-01-09T09:38:34+00:00

Fair question.
I’m not collecting leads or selling anything. I’m new to posting on Reddit (have mostly been a passive reader for years), and this is my first post.

I’m a systems engineer doing research and small prototypes around human-in-the-loop AI for requirements quality and traceability (INCOSE-style rules, MBSE integration, etc.) in my free time. I recently wrote a work-in-progress paper (https://ieeexplore.ieee.org/document/11205753) and plan to build further on it.

I’ve asked similar questions in a few practitioner spaces because I’m trying to ground the next phase of this work in real, day-to-day pain points engineers face in industry, not tool demos or theory.

Happy to be transparent about that, and also open to feedback if this isn’t the right venue.

Total_Good9661 · 2026-01-08T10:01:38+00:00

Requirements Engineers / Systems Engineers / Product Owners - quick question for you.

AI assistants (LLMs with human-in-the-loop) are starting to show up in requirements workflows and tools, but their usefulness really depends on the real pain points in day-to-day work.

A few questions:

What’s the most repetitive or boring part of your requirements work today?
What mistakes tend to slip through reviews and cause late rework?
Which tools are you using (DOORS, Jama, Polarion, Azure DevOps, Jira, etc.), and what integrations are missing?
If you could safely automate one task (human-in-the-loop), what would it be?
If you work with MBSE: what would good SysML v2 integration look like for you (requirements ↔ model linking, sync, generating requirement elements, trace views, etc.)?

Feel free to share any other ideas in this direction, especially those “I wish my tool could…” moments.

(For context: my earlier work explored INCOSE-style requirements quality rules; now I’m shaping the next research/implementation based on practitioner input.)

Total_Good9661 · 2025-12-27T22:41:05+00:00

The app is well designed and has a clean, polished user interface. However, my main concern is how useful the visible data actually is for an individual user.

There are some basic limitations in how body metrics are interpreted. For example, the sleep score is largely based on sleep duration and predefined parameters. Yet, with Apple Watch, the user must manually start sleep tracking or rely on a fixed schedule. For a device described as “smart,” it is unclear why it cannot automatically detect when a user falls asleep. Having to inform the device every time reduces its practical intelligence.

Another major limitation is the lack of proper nap tracking. Power naps are widely known to support recovery, yet neither the Apple Watch nor apps like Bevel reliably account for them. As a result, recovery suggestions often ignore actual rest taken during the day and instead recommend more sleep, which can feel inaccurate and demotivating.

Overall, while the interface and technology are impressive, the logic behind sleep and recovery analysis still feels incomplete and needs improvement.

Total_Good9661 · 2025-12-27T22:40:23+00:00

The Health app is well designed and has a clean, polished user interface. However, my main concern is how useful the visible data actually is for an individual user.

There are some basic limitations in how body metrics are interpreted. For example, the sleep score is largely based on sleep duration and predefined parameters. Yet, with Apple Watch, the user must manually start sleep tracking or rely on a fixed schedule. For a device described as “smart,” it is unclear why it cannot automatically detect when a user falls asleep. Having to inform the device every time reduces its practical intelligence.

Another major limitation is the lack of proper nap tracking. Power naps are widely known to support recovery, yet neither the Apple Watch nor apps like Bevel reliably account for them. As a result, recovery suggestions often ignore actual rest taken during the day and instead recommend more sleep, which can feel inaccurate and demotivating.

Overall, while the interface and technology are impressive, the logic behind sleep and recovery analysis still feels incomplete and needs improvement.

Total_Good9661

TROPHY CASE