A Prompt-Based Modular Architecture for GPT Tone Alignment (No Memory, No Fine-Tuning)

ChatGPT Use cases and examples
, , , ,
1

:brain: 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.

:white_check_mark: What’s inside

:studio_microphone: Voice Tone Companion

  • Ensures tone consistency
  • Supports emotional safety & dialog regulation
  • Works fully through prompt-layer logic

:card_index_dividers: SMRS – Synchronous Meeting Recording & Summarization

  • Real-time ASR, semantic grouping, summarization
  • Task extraction aligned to human conversation flow

:puzzle_piece: 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

:puzzle_piece: Fully composable. Prompt-based. Zero fine-tune.

:paperclip: Full proposal and radar chart

:speech_balloon: I’d love feedback or discussion — especially from anyone working on modularity, agent orchestration, or tone stability.

Best,
Y.H. Tsai
:e_mail: b9902048@gmail.com
:bird: @a930529

:wrench: 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 :sweat_smile:
Here’s the full proposal link (finally!):

:paperclip: Full proposal and radar chart

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!

A radar chart compares the capabilities of modular GPT (blue line) and non-modular GPT (orange dashed line) across eight performance categories, showing the modular GPT outperforms in all areas. (Captioned by AI)
A radar chart compares the capabilities of modular GPT (blue line) and non-modular GPT (orange dashed line) across eight performance categories, showing the modular GPT outperforms in all areas. (Captioned by AI)1771×1589 316 KB

:bar_chart: 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.