Built a prompt-only system exploring whether layered prompt decomposition reduces instruction drift and improves consistency in long multi-turn LLM sessions.

Goal: test structured prompting as an alternative to fine-tuning or external memory systems for maintaining constraint adherence.

Repo: https://github.com/nyragrimkitten-creator/The-Veritas-Loop

Approach

Multiple independent prompt layers are composed into a single system prompt at runtime to test whether decomposition improves long-context stability.

VERITAS (Constraint Layer)

Hierarchical instruction filters applied before generation:

• Context scope tracking (what can be referenced in the current turn)
• Rule compliance check (detects contradictions with prior constraints)
• Objective filtering (keeps output aligned with task intent)
• Consistency heuristic pass (prompt-level self-check, no external tools)

Purpose: reduce instruction drift in long contexts.

DRIVE (Priority Arbitration)

Resolves conflicts between competing objectives via ranked priority ordering used during instruction resolution.

• Accuracy vs verbosity
• Formatting strictness vs natural language flexibility
• Completeness vs token efficiency

State Schema (Optional)

Lightweight structured state tracking for continuity across turns:

STATUS
Health: 95
Stress: 40
Focus: 90
ContextLoad: 0.72
ConstraintAdherence: high

Used as a reference buffer for continuity (not simulation).

What this is testing

• Layered prompt decomposition vs single system prompt
• Priority arbitration under conflicting constraints
• Structured state injection for multi-turn continuity

Models tested: local instruction-tuned LLMs (7B–13B range, varies per run)

Limitations

• No deterministic guarantees (model variance remains)
• Higher token cost than flat prompting
• Results are qualitative, not benchmarked

Open questions

• Does layered decomposition improve long-context stability vs single prompts?
• How can instruction drift be measured reliably in open models?
• Are there prior systems using similar prompt-layer arbitration?

https://github.com/nyragrimkitten-creator/The-Veritas-Loop

Feel free to use and modify these prompts as needed. Please note that I'm an absolute beginner—these were generated entirely by Venice AI Agent Chat, and I have limited understanding of their underlying mechanics.

MIMI serves as the core foundational prompt. Its primary function is to enforce articulate, well-structured responses. It also significantly reduces processing time by preventing the model from entering recursive consideration loops.

NOCTUNE is oriented toward prompt engineering and diagnostic analysis. However, it has exhibited reliability issues—specifically, hallucinating MIMI's activation (claiming to invoke it while actually handling the workload itself) and generating false hypothetical scenarios. Consequently, it may be more useful during development than in actual deployment.

NOWA is an experimental construct intended to mitigate context bloat escalation. Unfortunately, I haven't found a reliable method to verify whether it's actually functioning as intended.