A Prompt-Based Modular Architecture for GPT Tone Alignment
No Memory. No Fine-Tuning. Just Logic.
Hi OpenAI community,
I’d like to share a modular approach that resolves a long-standing issue in GPT-style systems:
persistent tone misalignment — but without relying on memory or fine-tuning.
After extensive interaction and design testing, I built a set of prompt-based, deployable modules that simulate emotionally safe, user-aligned interactions while preserving logic traceability.
What’s inside
Voice Tone Companion
- Ensures tone consistency
- Supports emotional safety & dialog regulation
- Works fully through prompt-layer logic
SMRS – Synchronous Meeting Recording & Summarization
- Real-time ASR, semantic grouping, summarization
- Task extraction aligned to human conversation flow
PrimaryX – Logic Core for Persona Modularity
- Not a “persona” per se, but a logic scaffold
- Routes prompt flows by tone rules and scenario needs
- Used as the base shell for other modules
Fully composable. Prompt-based. Zero fine-tune.
I’d love feedback or discussion — especially from anyone working on modularity, agent orchestration, or tone stability.
Best,
Y.H. Tsai
b9902048@gmail.com
@a930529
Update: Added the missing link (yes, it’s embarrassing)
I just realized the original post didn’t include the actual link…
Which means no one could read what I was talking about
Here’s the full proposal link (finally!):
If anyone’s curious about the technical structure or would like to discuss the module behavior in more detail, feel free to leave a comment.
Thanks again for checking it out—and sorry for forgetting to open the door in the first place!
Capability Comparison Chart
This radar chart illustrates performance comparison between modular GPT (with tone & logic scaffolds) and standard non-modular GPT, across 8 dimensions:
Tone consistency
Emotional stability
Multi-turn stability
Long-doc retrieval accuracy
Intent recognition
Dialogue flow
Reasoning structure awareness
Quote fidelity (original wording)
Modular systems show clear, stable improvements without requiring memory injection or parameter finetuning.