By Vince Vangohn (Vincent Chong)

This framework and its terminology — Meta Prompt Layering (MPL) and Semantic Directive Prompting (SDP) — were originally developed and published by Vince Vangohn (Vincent Chong) as a language-based semantic control architecture for LLMs.

1. Motivation：Treating LLMs as Semantic Substrates

Prompt engineering today is dominated by tactical hacks — tricks to get better responses, better completions, more accurate formatting.

But what if we treated LLMs not just as reactive text predictors, but as semantic substrates — systems capable of sustaining internal structure, tone, and modular behavior across time?

This is not a call for plugin extensions, memory tools, or architecture changes.

It’s a proposal to treat language itself as architecture, and to build logic-bearing, tone-persistent, identity-aware structures inside LLMs using prompts alone.

2. Core Framework

To that end, I’ve been developing a two-part framework:

Meta Prompt Layering (MPL)

MPL is a multi-layer prompt architecture where each prompt segment plays a distinct functional role.

Each “layer” encodes a specific semantic purpose:

• Layer 1 – Identity & Tone Setup
• Layer 2 – Task Framing
• Layer 3 – Logic Routing
• Layer 4–6 – Modular Behaviors (e.g., emotional stability, reflection, constraint checking)
• Layer 7 – Aggregation & Loopback

These layers are designed to simulate stability across turns, including:

• Recursion without memory
• Continuity of tone and identity
• Semantic feedback loops

It turns the prompt into a kind of operating field — not just input, but structure.

Semantic Directive Prompting (SDP)

If MPL defines where semantic roles go, SDP defines what logic governs those roles.

SDP is a method for embedding executable behavior inside natural language using structured, directive phrasing.

Example:
“Enable a tone regulator that compares this output to the previous. If deviation exceeds 15%, reframe in prior style.”

Each SDP directive includes:

• Module Tagging (e.g., tone regulator)
• Semantic Intention (e.g., maintain stylistic consistency)
• Linguistic Logic (e.g., deviation threshold and trigger action)

In effect, SDP lets us "program" LLM behavior — using grammar as logic syntax.

3. Combined Dynamics

When MPL and SDP are combined:

• Prompt layers become functional scaffolds
• SDP turns sections into modular agents
• The model simulates logic without plugins or memory

The goal is not to simulate cognition per se, but to simulate behavior that feels semantically coherent — like an internal program written in language itself.

4. Why This Matters

This framework explores a middle path between reactive prompting and full agent design:

• It requires no tool integrations
• It’s portable across models
• It’s interpretable and composable

More importantly, it raises a deeper question:

What if language is not just what LLMs produce, but what they run?

5. Invitation

If you're working on prompt systems, semantic structuring, or recursive identity mechanisms — or simply curious about language as control — I’d love to connect.

I’ll be publishing scaffolds, diagrams, and further technical notes soon.

— Vince Vangohn