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So, I’ve been working on this framework that uses symbolic tags to simulate how an LLM might handle tone, stress, or conflict in something like onboarding or support scenarios. Stuff like:
csharpCopyEdit[TONE=frustrated] [GOAL=escalate] [STRESS=high]
The idea is to simulate how a human might react when dealing with a tense interaction—and see how well the model reflects that tension or de-escalates over time.
I’ve got a working Python prototype, some basic RAG setup using vector DB chunks, and early behavior loops running through things like GPT-4, Qwen, and OpenHermes, Mythos, and others. I’m not doing anything crazy—just chaining context and watching how tone and goal tags affect response clarity and escalation.
But I’m hitting some walls, and I’d love feedback or tricks if anyone’s dealt with this stuff.
Basically: I’m trying to make LLMs “feel” more consistent across interactions—especially when people are rude, confused, or anxious. Not for fun, really—just because I’ve worked retail for years and I want to see if models can be trained to handle the same kind of stress better than most people are trained.
If you’ve got tips, tools, workflows, or just opinions on what not to do, I’m all ears. I’m solo on this and figuring it out as I go.
Here’s the repo if you’re curious or bored:
🔗 https://github.com/Silenieux/Symbolic-Reflection-Framework
Finally; I know I’m far from the first, but I have no formal training, no degrees or certs, this is done on my free time when i’m not at work. I’ve had considerable input from friends who are not tech savvy which has helped me push it to be more beginner friendly.
No sales pitch, no “please hire me,” just trying to build something halfway useful and not fry my GPU in the process. Cheers.
submitted by /u/Silenieux
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